Rough Set Data Analysis

Rough set data analysis 1.0 Ivo Düntsch Günther Gediga School of Information and Software Engineering FB Psychologie / Methodenlehre University of Ulster Universität Osnabrück Newtownabbey, BT 37 0QB, N.Ireland 49069 Osnabrück, Germany [email protected] [email protected] June 28, 1999 1 Introduction Rough set data analysis (RSDA), developed by Z. Pawlak and his co–workers in the early 1980s [47] has become a recognised and widely researched method with over 1100 publication to date [54, Appendix 1]. The conceptual foundation of the RSDA model is the consideration that all perception is subject to granularity, and that the ability to classify is at the root of human intelligence: “Our claim is that knowledge is deep–seated in the classificatory abilities of human beings and other species. For example, knowledge about the environment is primarily manifested as an ability to classify a variety of situations from the point of view of survival in the real world : : : Classification on more abstract levels, seems to be a key issue in reasoning, learning and decision making, not to mention that in science classification it is of primary importance too.” [48] The main thrust in current applications of rough set theory are  Attribute reduction,  Rule generation,  Prediction.  The ordering of authors is alphabetical, and equal authorship is implied. 1 Table 1: The position of RSDA in Soft Computing [35] Deductive Inductive Microscopic, primarily numeric Macroscopic, descriptive and numeric Chaos theory Neural networks, genetic algorithms Fuzzy methods RSDA Many examples of applications of RSDA to process control, economics, medical diagnosis, biochemistry, environmental science, biology, chemistry psychology, conflict analysis and other fields can be found in [30, 54, 69]. For further information of developments up to 1999, we recommend the list given in [25]. RSDA is generally regarded as part of the “Soft Computing” paradigm, and in a recent book [35], it is treated as one of the five key “non traditional AI areas” (Table 1). However, while other soft methods require additional model assumptions such as the representability of the collected sample, prior probabilities, fuzzy functions, or degrees of belief, RSDA is unique in the sense that it is“non – invasive” i.e. that it uses only the information given by the operationalised data, and does not rely on other model assumptions. In other words, instead of using external numbers or other additional parameters, rough set analysis utilises solely the structure of the given data: “The numerical value of imprecision is not pre-assumed, as it is in probability theory of fuzzy sets - but is calculated on the basis of approximations which are the fundamental concepts used to express imprecision of knowledge : : : As a result we do not require that an agent assigns precise numerical values to express imprecision of his knowledge, but instead imprecision is expressed by quantitative concepts (approximations). [48] In this sense, RSDA, as a measurement process, can be regarded as “nominal scaling” [74]. In order to position RSDA in the data modelling process, we follow [19] in assuming that a data model consists of 1. The “acting subject”. D of interest chosen by the agent. 3. An empirical system E , which consists of a body of data and relations among the data, and a mapping D ! E , called operationalisation. 4. A numerical model M, and a mapping E ! M, called representation. 2. A domain e : m : As we can see in Fig. 1, the acting subject with her/his objectives is the central part of the modelling process, and the first, and, possibly, major factor of subjectivity: Agents choose an operationalisation according to their objectives and their subjective view of the world. This is a fact which is encountered by any method of data analysis: Operationalisation is necessary to be able to talk about a domain, but it 2 Figure 1: The data modelling process Subject with objectives  Domain of Operationalisation interest - - Numerical system ? Empirical system Representation raises two questions; first, whether the elements and relations of the result of the operationalisation, i.e. the empirical model , are representative for the domain (consistency problem), another whether the choice of features covers the relevant aspects of (completeness problem). E D D The next step, the numerical models, are a reduction of the empirical model, and thus of the domain of interest; going from the empirical to the numerical model, often results in further decontextualisation. We invite the reader to consult [83, Section 2] for further comments on the relationship of RSDA to numerical models. RSDA takes place on the level of the empirical model, and the main motto of RSDA is (1.1) L ET THE DATA SPEAK FOR THEMSELVES : An operationalisation of the type O BJECT 7! ATTRIBUTE( S) relationship in form of a data table is supposed to 1be given (with all its underlying assumptions), and subsequent analysis uses only the information contained in this table. This is in contrast to most statistical (or other) analysis methods. Even the bootstrap technique, which is discussed in the rough set context in [75] needs additional model assumptions, because one has to suppose that the percentages of the observed equivalence classes are representative estimators of the latent probabilities of the equivalence classes in the population. We shall see below that dependencies and decision rules are always extracted (or learned) from existing systems, and they are not part of the design process as is the case, for example, with relational databases. Since the field of RSDA has grown rapidly within the past five years, and there are many ramifications and generalisations, it is impossible to describe in detail every thread in the area, and we will have to be content to outline the major issues and developments. The paper is structured as follows: We will start by describing the basic RSDA model, its mathematical foundations, and its knowledge representation in some detail. We will then describe how RSDA tackles major issues of data analysis: 3  Attribute reduction and rule generation,  Significance testing and rule validation,  Model selection,  Data discretisation, including grouping of qualitative values. This is followed by a brief description of further development in RSDA. We conclude with a summary and an outlook to further development. 2 The basic model of RSDA The mathematical machinery of RSDA is derived from the assumption that granularity can be expressed by partitions and their associated equivalence relations on the set of objects, also called indiscernability relations in this context. Suppose that U is a nonempty set. A partition or classification of U is a family P of nonempty subsets of U such that (2.1) Each element of U is contained in exactly one element of P : Recall that an equivalence relation  on a set U is a reflexive, symmetric, and transitive binary relation on U , i.e. for all x; y; z 2 U , we have xx; Reflexivity xy implies yx; Symmetry xy and yz imply xz: Transitivity Each partition P induces an equivalence relation  on U by setting (2.2) xy () x and y are in the same class of P : Conversely, each equivalence relation  on U induces a partition P of U whose classes have the form x = fy 2 U : xy g: By some abuse of language, we also speak of the classes of an equivalence relation when we mean the classes of its associated partition, and call x the class of x modulo . The interpretation in rough set theory is that our knowledge of the objects in U extends only up to membership in the classes of , and our knowledge about a subset X of U is limited to the classes of  and their unions. This leads to the following definition: For X  U , we say that (2.3) X def = [fx 4 : x  X g Figure 2: Rough approximation [fx is the lower approximation or positive region of X, and X (2.4) def = : x 2 Xg is the upper approximation or possible region of X . If X  U is given by a predicate P and x 2 U , then 1. x 2 X means that x certainly has property P , 2. x 2 X means that x possibly has property P , 3. x 2 U n X means that x definitely does not have property P . The area of uncertainty extends over X n X; and the area of certainty is [ X: A rough subset of U is a pair hX; X i, where X  U . A subset X of U is called definable if X X = X. In this case, X is empty or a union of equivalence classes of , and the area of uncertainty is ;. An equivalent – and, possibly, more transparent – way of describing the information given by hU; i is to consider pairs hX; X i; this gives the area of positive certainty in the first component, and that of negative certainty in the second one, and it is closer to the machine learning situation of positive and negative examples. It also avoids certain statistical difficulties which arise when using the upper approximation. It has been shown that the collection of all rough subsets of U can be made into a regular double Stone algebra [6, 10, 24, 56], and that these algebras can serve as semantic models for three valued 5 Table 2: Fisher’s Iris data [18] Object 1 s2 s3 s4 s6 s7 s Sepal Sepal Petal Petal length width length width 50 33 14 2 46 34 14 3 65 28 46 15 62 22 45 15 67 30 50 17 64 28 56 22 <143 other specimen> Class 1 1 2 2 3 3 Łukasiewicz logic [9]. The connections between algebra and logic of rough set systems are explored in some detail in [45]. A different logical approach to the rough set model is given by [41] and [31]: There, the lower approximation is considered a necessity operator 2 , and the upper approximation a possibility operator 3. It may be also worthy of mention that RSDA can be interpreted in Shafer’s evidence theory [60] in such a way that beliefs are obtained internally from the lower approximation of a set, and plausibility from its upper approximation [61, 63]. 3 Knowledge representation Knowledge representation in RSDA is done via information systems, which are a form of data table. More precisely, an information system I = hU; ; Vq; fq iq2 consists of 1. A finite set U of objects, 2. A finite set of attributes, 3. For each q 2  A set Vq of attribute values,  An information function fq : U ! Vq . Table 2 which shows part of the famous Iris data [18] is an example of such a system: U consists of 150 specimen of Iris flowers; there are five attributes, namely, sepal length, sepal width, petal length, petal width, and an attribute class. The sets Vq of attribute values for the first four attributes consist of lengths, measured in millimetres, and the attribute class takes its values from the set f1; 2; 3g, which 6 code the species Setosa, Versicolor, Virginica. We think of the descriptor fq (x) as the value which object x takes at attribute q ; thus, for example, fsepal length (s1) = 50; fpetal length(s2 ) = 14; fclass (s3) = 2: What we have above is, in fact, a decision system: Besides four independent features, there is a dependent decision attribute class. We observe that in information systems, each value fq (x) is unique, and there can be no missing values. A generalisation of the information system above are the multi–valued information systems, introduced in [44]. There, the information functions take on set values, i.e. for each q 2 and x 2 U , fq (x)  Vq: These can cater for non–deterministic information and null values. We refer the reader to [43] for a more detailed description of multi–valued information systems and their algebraic and logical properties. 4 Attribute reduction and rule generation The discovery of data dependencies and the reduction of the number of attributes is one of the major topics in data mining. As we shall see below, RSDA offers purely structural methods to achieve both. In the sequel, we let I = hU; ; Vq; fq iq2 be a generic information system. As a running example, we shall use the decision system of credit card applications shown in Table 3. We let = f 1; 2; 3; 4g, and consider the decision attribute separately. Table 3: Credit card applications Condition attributes Applicant a1 a2 a3 a4 a5 a6 a7 a8 1 2 3 4 Account Balance Employed Monthly outgoing d Decision bank bank none other other other bank none medium low low high medium high high low yes yes yes yes yes yes no no low high medium high high low medium low accept reject reject accept reject accept accept reject Attribute reduction in RSDA is achieved by comparing equivalence relations generated by sets of attributes. With each Q  we associate an equivalence relation Q on U by xQ y if and only if fq (x) = fq (y ) for all q 7 2 Q. Intuitively, xQ y if the objects x and y are indiscernible with respect to the values of their attributes from Q. It is not hard to see that for P; Q  P  Q implies Q  P : Thus, the finest equivalence relation obtained this way is  , and ; is the universal relation U  U. The partition of  in our system of Table 3 (without the decision attribute d) is the identity relation; all eight applicants are pairwise distinguishable by the given attributes. We use these equivalence relations to express dependency among attribute sets in the following way: Suppose that P; Q  ; P is called dependent on Q – written as Q ) P – if Q  P . In this case, every class of P is a union of classes of Q , so that membership in a class of Q determines membership in a class of P . In other words, the partition of U induced by P can be expressed by the partition induced by Q, so that the world according to P is coarser than the world according to Q. If P = fpg, we usually just write Q ) p. A set P Q 1. P is called a reduct of Q, if = Q , 2. For each R ( P we have R 6= Q. In other words, P  Q is a reduct of Q, if P is minimal among all subsets of Q which generate the same classification as Q; the attributes within a reduct are independent, and none of them can be omitted for the description of Q. Reducts produce deterministic rules. It is not hard to see, that each Q  has a reduct, though this is usually not unique. The intersection of all reducts of Q is called the core of Q, written as ore(Q), and the elements of the core of Q are called indispensable for Q. If q 2 Q, and q is not an element of any reduct of Q, then we call q redundant for Q. If Q = we just speak of reducts of I , core of I etc. The set of all reducts of I is denoted by Red(I ). Reducts correspond to keys of a relational database; consequently, as was pointed out in [57] the problem of finding a reduct of minimal cardinality is, in general, NP-hard, and finding all reducts has exponential complexity [66]. A transparent method to find the core and the reducts of an information system I via discernibility matrices was given in [66]: Define a discernibility function Æ : U  U ! 2 by (4.1) ( ) def = fq 2 : fq (a) 6= fq (b)g: Æ a; b The function Æ leads to a pseudo-metric, since (4.2) (4.3) (4.4) = b implies jÆ (a; b)j = 0: jÆ(a; b)j = jÆ(b; a)j: jÆ(a; )j  jÆ(a; b)j + jÆ(b; )j: a If  is the identity, then the converse of (4.2) holds as well, and the assignment ha; bi 7! jÆ (a; b)j is a metric. Now, 8 Proposition 4.1. [66] 1. The core of I is the set fq 2 : Æ(a; b) = fqg for some a; b 2 U g: 2. P  is a reduct of I if P is minimal with respect to the property P for all a; b 2 \ Æ(a; b) 6= ; ( ) 6= ;. ; Æ a; b The discernibility matrix of the system of Table 3 without the decision attribute is given in Table 4. Table 4: Discernibility matrix 1 2 3 4 5 6 7 1 2 3 4 5 6 7 8 2; 4 1; 2; 4 1; 2; 4 1; 4 1; 2 2; 3; 4 1 ; 2; 3 1; 4 1; 2 1; 2 1; 2; 4 2; 3; 4 1 ; 3; 4 1; 2; 4 1; 2; 4 1; 2; 4 1; 2; 3 3; 4 4 3; 4 1; 2; 3; 4 1; 3; 4 1 ; 2 ; 3; 4 1 ; 2 ; 3; 4 1 ; 2; 3 2 2; 4 1 ; 2; 4 The entries h4; 5i; h4; 6i show that the core of the system I is f 2; also the only reduct of I . 4 g, and h5; 6i shows that this the The relations among the classes of the equivalence relations associated with attribute sets can be used to generate decision rules; for notational simplicity, we suppose that we have a set Q = fq1 ; : : : ; qn g of independent attributes, and a single dependent attribute d; this is no restriction of generality, since we are using only the partition information of d , and thus, d can be a “composite attribute”, obtained from some P  . We assume that the partition induced by Q is fX1; : : : ; Xsg, and the one induced by d is fY1; : : : ; Ytg. With each Xi we associate the set Mi = fYj : Xi \ Yj 6= ;g. Since the sets Y1 ; : : : ; Yt partition U , we see that If x 2 Xi ; then x 2 Yj1 or : : : or x 2 Yji(j) ; (4.5) where Mi = fYj ; : : : Yj g. 1 (aj )1in , where x 2 Now recall that each class Xi of Q corresponds to a feature vector Xi if and only if fq1 (x) = a1 and : : : and fqn (x) = an ; similarly, x 2 Yj if i(j ) 9 and only if fd (x) form = bj for some bj 2 Vd. If we translate condition (4.5), this leads to a rule of the If fq1 (x) = a1 and : : : and fqn (x) = an ; then fd (x) = bj1 or : : : or fd (x) = bji(j) : (4.6) It follows that RSDA rules are able to express deterministic as well as indeterministic information: If some class Xi of Q intersects exactly one Yj , then Xi  Yj , and the value of d of any x 2 Xj is uniquely determined; otherwise, fd (x) may be in any class contained in Mi , and we have a proper disjunction on the right hand side of (4.6). A class Xj is called deterministic, if it is contained in some Yj , otherwise, we call it indeterministic. If all classes Xj are deterministic, then, clearly, Q  d , and d is dependent on Q. We will write Q ! d for the collection of rules (4.5), and, with some abuse of language call Q ! d a rule of the information system I . Let us consider again our example of Table 3. If Q = f g and d is the decision attribute, we have X1 = fa1 ; a5g; X2 = fa2; a3; a8g; X3 = fa4 ; a6; a7g Y1 = fa1; a4; a6; a7g; Y2 = fa2 ; a3; a5; a8g: 2 X2 and X3 are deterministic classes, while X1 is not, and we obtain the following decision rules with respect to d: (4.7) If bank balance is medium, then accept or reject: (4.8) If bank balance is low, then reject. (4.9) If bank balance is high, then accept. By taking into account attribute 4 we can split X1 in  2; 4 , and refine rule (4.7) by (4.10) If bank balance is medium and monthly expense is low then accept, otherwise, reject. By using 1 (4.11) and 2, we obtain another rule system: If bank balance is medium and account is bank then accept, otherwise, reject. We refer the reader to [21, 22, 62, 71, 85] for various methods to generate RSDA rules. 5 Approximation measures Even though RSDA is a symbolic method of analysis, it uses counting information provided by the classes of the equivalence relations under consideration. The inherent statistics of an approximation space hU; i are the two approximation functions (5.1) (5.2) jX j + j X j ; jU j def jX j  (X ) = jX j (for X 6= ;); X ) def = ( 10 see [48], p. 16ff. If  is understood, we shall usually omit the subscripts. (X ) is the percentage of objects of U which can be correctly classified with the knowledge given by  as being in X or not, while  (X ) expresses the degree of completeness of our knowledge of X . It  was shown in [15] that the approximation is a manifestation of the underlying statistical principle of RSDA, namely, the principle of indifference: Within each equivalence class, the elements are assumed to be randomly distributed. We can generalise the approximation functions to partitions with more than two classes in the following way: As a measure of the quality of an approximation of a partition P by a set Q of attributes we define (Q ! P ) by (5.3) (Q ! P ) = P jX Q j jU j ; X 2P P is induced by P for some P  , we will write (Q ! P ) instead of (Q ! P ). If X 2 P , then X Q is the set of all elements of X that are correctly classified with respect to the attributes in Q, and (Q ! P ) is the ratio of the number of all certainly P – classified elements of U In case with the information provided by the attributes in Q to the total number of elements of U . Note that Q ) P implies (Q ! P ) = 1. Suppose that for Q; P  , Q is a reduct of P , so that Q ) P , and Q n fq g 6) P for any q 2 Q. In rough set theory, the impact of attribute q on the fact that Q ) P is usually measured by the drop of the approximation function from 1 to (Q n fq g ! P ): The larger the difference, the more important one regards the contribution of q to the rule [48, p.58]. While the approximation quality measures the global classification success in terms of the equivalence classes, one can use the same principle for elements and define rough membership functions [49]: For each X  U , let X : U ! [0; 1℄ be a function defined by X (x) = (5.4) It is easy to see that for all X; Y (5.5) (5.6) (5.7) (5.8)  U, X jx \ X j : jxj 8 <1; (x) = : 0; iff x 2 X; iff x 62 X; U nX (x) = 1 X (x); X [Y (x)  max(X (x); Y (x)); X \Y (x)  min(X (x); Y (x)): The cases, where equality holds in (5.7) and (5.8) - and when, consequently, X is a fuzzy membership function, as well as an efficient algorithm to compute rough membership functions are given in [49]. Unlike fuzzy membership, the X values are obtained from the internal structure of the data, and no outside information is needed. 11 Rough sets and fuzzy sets are geared towards different situations: While at the heart of RSDA is the concept of granularity, mathematically expressed by classes with crisp boundaries, within which no information is available, fuzzy sets describe vagueness of a concept where boundaries among classes are ill–defined. Frameworks to combine the two points of view have been proposed, among others, in [7, 70, 82]. Hybrid systems, where RSDA is used as a pre-processing device for fuzzy methods have been given, for example, in [50, 72] 6 Rule significance and validation In the early development of RSDA, the static point of view was predominant: Reducts which were obtained from one sample of data were studied, along with the contribution of single attributes, measured by the drop of the approximation quality, e.g. [68, 73]. Over time, it became clear, however, that for prediction, i.e. the classification of new elements, these tools were not well suited and other measures had to be found. “The rough set methods developed so far are not always sufficient for extracting laws from decision tables. The set of all decision rules generated from all conditional attributes can be too large and/or can contain many chaotic rules not appropriate for unseen object classification” [1]. Indeed, a re-analysis of the results of [68] and [73] showed that not all the claimed rules were useful, and that some potentially valuable rules were overlooked [12]. Any rule based system faces the problems of rule significance and rule validity. Whereas significance tests measure the probability that a rule is due to chance, i.e. random behaviour, validation of a rule system investigates, how well the particular hypothesis, expressed by the rule, is replicable. Clearly, both problems need to be addressed before RSDA can claim to be a fully fledged instrument for data analysis and prediction. Of particular interest are those significance testing and validation procedures which stay within the non–invasive RSDA paradigm of (1.1), and do not rely on extra model assumptions. One might suppose that the approximation quality is a suitable tool, since it measures classification success; however, it has been shown in [12], that a high approximation quality is not a guarantee that the result of a rough set analysis is either significant or valid. If, for example, rough set analysis discovers a rule of the form Q d, and if the rule is based on only a few observations, classification may be due to chance. A similar observation can be made with regard to the influence of one attribute in a set of independent attributes. ) A solution of the significance problem was given in [12] on the basis of randomisation procedures. These are particularly appropriate to RSDA as a non–invasive method, since they do not require any representability assumption and use only the information of the given sample [17, 33]. A special case of the procedures developed in [12] is the investigation of the contribution of single attributes on a 12 rule Q ! d; an attribute q 2 Q is called conditionally casual, if, loosely speaking, there are only a few observations in which q is needed to predict d. Removing conditionally casual attributes reduces the overfitting of data which is common in the traditional RSDA approach based on . Significance of a rule is a minimal but necessary requirement for evaluating the predictive power of a rule Q ! d. To test the prediction quality of a rule, additional validation is needed. Rule validation in RSDA is usually performed by customary cross–validation methods. It can be seen, however, that the jackknife validation (“leave-one-out”) may result in an overly optimistic estimation of prediction success [3]. Another problem with this method, particular to RSDA, is the case when the element which is left out can only be predicted by its own rule. In this case, invasive techniques such as the adoption of metric information (Hamming distance, Euclidean distance etc.) need to be applied, which assume more than the data itself tell us, and are outside the RSDA paradigm (1.1). A non-invasive validation technique is based on a significance test [12]. The reasoning behind the method is the assumption that prediction success should not depend on the way the data set is split up into a training and a testing set: A new attribute SPLIT is added and randomly distributed over half of the data set. If SPLIT is significant in more than
100% of the simulated data sets, then the split is not random, and the prediction success is dependent on the choice of training and testing set. It follows that the rule cannot be validated. 7 Model selection Attribute reduction is an inherent feature of RSDA, and usually has no unique solution. If several attribute sets exist from which prediction is possible, then the problem arises which of these are most suitable by some predefined criterion. We will present two very different recent methods of model selection. The first one is selection by dynamic reducts [1]: “The underlying idea of dynamic reducts stems from the observation that reducts generated from information systems are unstable in the sense that they are sensitive to changes in the information system introduced by removing a randomly chosen set of objects. The notion of dynamic reduct encompasses the stable reducts, i.e. reducts that are the most frequent reducts in random samples created by subtables of the given decision table” [65]. For I = hU; ; Vq; fq ; diq2 and U 0 is a family of subsystems of I , then (7.1)  U , we call I = hU ; ; V ; f ; di 0 0 DRed(I; F) = Red(I ) \ 0 q q q2 a subsystem of I . If F \fJ : J 2 Fg is called the family of F-dynamic reducts of I . It is easily seen that in most cases, this is too restrictive for practical use, and thus, a threshold is introduced: Let 0    1. The family of (; F)-dynamic reducts of I is defined by (7.2) D Red(I; F) = fQ 2 Red(I ) : 1   sF (Q)g 13 where sF (Q) = (7.3) jfJ 2 F : Q 2 Red(J )gj jFj is the F- stability coefficient of Q. Model selection proceeds in four steps:  n; 1  kj  U2 , and a threshold . For each 1  j  n generate a subsystem Ij of I by randomly deleting a kj objects of U , and set F = fIj : 1  j  ng. Find the reducts for I and each Ij . Choose all reducts Q with 1   sF (Q) for further processing. 1. Choose numbers n, kj ; 2. 3. 4. j j j From these “true dynamic reducts”, decision rules for classification are computed, and the final decision is taken by “majority voting”. The method of dynamic reducts employs a kind of internal cross-validation in order to improve the external prediction quality. We observe that the researcher has to make some subjective choices in step 1. of the procedure, which are not contained in the data. The huge complexity of step 3. forces applications of heuristic techniques, such as combinatorial or genetic algorithms. Extensive experiments reported in [2] show that the dynamic reduct approach fares considerably better than the traditional RSDA method and compares well with customary procedures. For extensions of the method and similar approaches we refer the reader to [2, 40, 65]. A different approach, reported in [14], does not rely on reducts at all – thus leaving the traditional RSDA way of reducing feature complexity – and uses information theoretic techniques and the minimum description length principle [58, 59]. The model selection criterion is an entropy value H (Q ! d) which aggregates   The complexity of coding the hypothesis Q, measured by the entropy H (Q) of the partition of its associated equivalence relation Q and The conditional coding complexity H (djQ) of d, given by the values of attributes in Q, so that (7.4) H (Q ! d) = H (Q) + H (djQ): The value H (Q ! d) is obtained in the following way: Suppose that Q generates the partition of U with classes Mi ; i  k, each having cardinality mi . In compliance with the principle of indifference – the statistical basis of RSDA – we suppose that the elements of U are randomly distributed within the classes, so that the probability of an element x being in class Mi is just jmUij . This leads to a probability distribution f ^i : i  kg defined by  ^i = mi jU j : 14 Suppose that the union V of deterministic classes of Q ! d is not empty, and that the deterministic classes are M1 ; : : : ; M . The underlying idea is the consideration that each observation y 2 U n V is the result of a random process whose characteristics are unknown; therefore, each such y should be viewed as a realization of an unknown probability distribution with uncertainty n1 log2 (jU j). This observed probability distribution is given by f ^i : i  + jU n V jg with 8 <^i; ^ def i=:1 jUj ; (7.5) if i  ; otherwise: We now define the entropy of rough prediction (with respect to Q ! d) as H (Q ! d) X^ i and have H (Q ! d) = = where = (Q i
log2( ^ ) i X i X i | 1 ^i
log2 ( ^i
log2 ( {z 1  ^i 1  ^i Knowledge )+ jU n V j
jU1 j
log2(jU j)
{z | ) + (1 } jU j}) : ) log2( Guessing ! d). This gives us H (djQ) = H (Q ! d) = (1
H (Q) jU j) ) log2 ( X i> ^i
log2 ( 1 ^i ): The importance of H (Q ! d) is due to the fact that it aggregates the uncertainty H (djQ) and the effort H (Q) of coding the hypothesis, i.e. the predicting elements. Thus, one can compare different attribute sets Qi in terms of a common unit of measurement, which cannot be done by a conditional measure of prediction success such as approximation or dynamic reducts. Furthermore, there are no additional parameters required, so that the method is well in accord with the RSDA basic principle (1.1). The model selection (and attribute reduction) process SORES (Searching Optimal Rough Entropy Sets) compares well with standard numerical methods, without using invasive model assumptions. Detailed results can be found in [14] and on the website http://www.psycho. uni-osnabrueck.de/sores/. It is worthy of mention that these methods are universal, and they take place before ad hoc tuning for specific situation can be applied. 8 Data discretisation Attributes with continuous values pose a severe problem for RSDA, indeed, for any rule based data analysis method: If there are many values, the equivalence classes belonging to the attribute set in 15 question may have many small classes, and a rule will be based on a few instances only. Therefore, its significance will be low, and its ability to classify new objects is rather limited. There are many methods to discretise a continuous domain, most of which require additional parameters and model assumptions, and thus, are outside the rough set paradigm. We invite the reader to consult [37, 38, 64] for such methods in the context of RSDA. The general claim that RSDA is not applicable to most real life problems, because it cannot handle continuous variables is not supported by recent research results [3], and [36, 37, 39] show that RSDA can be supplemented by numerical discretisation procedures. The success of applications of fuzzy controlling, which also requires discretisation of continuous data, shows that the distinction of “continuous data” vs. “discrete data” does not necessarily imply that there is a need for different “continuous methods” , respectively, “discrete methods”, to handle these different types of data. A filtering procedure which uses only internal information has been put forward in [13]. The main idea is to collect attribute values within one attribute domain, which do not split a class of the decision attribute; the procedure makes makes no use of the ordering of attribute values. For example, if there is a rule If q = 1.2 or q = 4.6 or q = 4.7, then d = green, then one can collect 1.2, 4.6, and 4.7 into a single attribute value of q . Formally, the descriptor function 0 fq : U Vq is replaced by a function fq : U 2Vq . ! ! The important feature of this procedure is that the internal dependency structure of the system is kept intact, and that no additional parameters are needed. Nevertheless, this simple “nominal” filtering method is surprisingly powerful, as its application to the Iris data set (Table 2) shows: Table 5 gives the filtering for the data. There, for example, the sepal length values 43 – 48 and 53 are collected into one common value 46; in this attribute, filtering reduces the number of classes from 35 to 22. Observe the large reduction in the number of classes of the petal attributes. Extensions of this method to multi-attribute filtering can be found in [79, 80]. 9 Extensions and variations of RSDA In this section we will briefly describe other directions into which RSDA has branched, some of which are only beginning to be investigated. The variable precision rough set model (VPRS), introduced in [83], is a generalisation of the original RSDA in the direction of relaxing the strict boundaries of equivalence classes; it assumes that rules are only valid within a certain part of the population, and it is able to cope with measurement errors: “Hypotheses derived from sample data should not : : : be based only on error – free classification rules observed in the sample data. Also, partially incorrect classification should be taken into account. Any partially incorrect classification rule provides valuable trend 16 Table 5: Filtering of Iris data [3] No of classes Attribute Filter Before After Sepal length: 43–48, 53 ! 46 66,70 ! 70 35 22 71–79 ! 77 Sepal width: 35, 37, 39–44 ! 35 23 16 20, 24 ! 24 Petal length: 10–19 ! 14 30–44,46,47 ! 46 43 8 50, 52, 54–69 ! 50 Petal width: 1–6 ! 2 10–13 ! 11 22 8 17, 20–25 ! 17 information if the majority of available data to which such a rule applies can be correctly classified” [83]. The first step in the VPRS analysis is a numerical form of set inclusion in analogy to the rough membership function of (5.4). If X; Y  U , then the relative classification error of X with respect to Y is the value (9.1) 8 <1 (X; Y ) = :0; We now choose a tolerance threshold 0  X (9.2) jX \Y j ; if X jX j otherwise. 0:5  Y () and set (X; Y )  Given an approximation space hU; i we define lower and upper and (2.4) by [ fx : x  X g [ def (9.3) X def (9.4) X = 6= ;; : – approximation in analogy to (2.3) = fx : (x; X )  g: In this spirit, one can define a – dependent approximation quality (Q ! P ), and rules. Details can be found in [83], and some application examples in [84]. – approximate The advantage of the VPRS is that it uses only two parameters, the external precision parameter and the internal approximation quality , to describe the quality of a rule system; its disadvantages are that precision and are partially exchangeable, and that there is as yet no theoretical background to judge which combination is best suited to the data. 17 A much more general form of “rough inclusion” was proposed in [51] which is modelled after Leś niewski’s mereology [28, and also the reprint [29]]. Mereology is an alternative to set theory which avoids the pitfalls of Russel’s paradox. Unlike set theory, which distinguishes between 2 and , its only connective is a “part of” relation P , which is assumed to be asymmetric, and transitive; thus, P is a strict partial order. It has been recognised that mereology is often more suitable as a language for reasoning about complex objects than first order logics based on Cantorian set theory; applications of mereological systems arise, for example, in spatial reasoning [4, 78] and natural language processing [32]. M A model of rough mereology is a structure = hX; ; n; F ui, such that X is a collection of objects,  : X  X ! [0; 1℄ a function, and n 2 X ; we denote by X + the set X n fng. Furthermore, + F u : 2X ! X is a function, called fusion, such that (9.5) x = F u(U ) () (8z 2 X + y )[(z; x) = 1 ) + (9w 2 U; t 2 X )((w; z ) = 1 ^ (w; t) = 1 ^ (t; x) = 1)℄: There are the following axioms: 8w 2 X )[(n; w) = 1 ^ ((w; n) = 1 ) (n; w) = 0)℄: ( (9.6) For all x; y; z 2 X , (9.7) (9.8) (x; x) = 1; (x; y ) = 1 ) (z; y )  (z; x): The distinguished n will be referred to as the null element 1 . The function  is called a rough inclusion, and it is interpreted as “x is part of y to degree (x; y )”. An example is the function U : 2U  2U ! [0; 1℄ defined by (9.9) 8 jX\Y j < jXj ; (X; Y ) = :;; if X 6= ;; otherwise. It is shown in [51] that rough mereology extends classical mereology, and that, furthermore, “ : : : we can interpret rough inclusions as global fuzzy membership functions on the universe of discourse which satisfy certain general requirements responsible for their regular mathematical properties.”. Rough inclusions are the basis for reasoning about the synthesis of complex systems from their simpler parts and have widespread applications, for example, computer-aided design, re-engineering, and distributed problem solving. We invite the reader to consult [65] for an overview of recent results in this area. 1 It may be interesting to remark, that Leśniewski was vehemently opposed to the introduction of a null element, [see e.g. 29, p.18ff]. 18 Table 6: State complexity # of attributes # of attr. 10 20 30 values log10 (states) 2 3.01 6.02 9.03 3 4.77 9.54 14.31 4 6.02 12.04 18.06 5 6.99 13.98 20.97 It has been argued that the condition of transitivity for an indiscernibility relation is too strong, viz. the “Sorites Paradox” [34]. Thus, similarity relations, which are reflexive and symmetric, but not necessarily transitive, have been studied inside RSDA in some detail (for example, in rough mereology), and many of the notions of indiscernibility based RSDA have been translated and adjusted to the new situation [23, 27, 55, 67]. The logical structure of the resulting systems has been investigated in [26, 76, 77]. Ordinal prediction takes into account numerical information of the domain of the decision attribute; its rules predict intervals rather than unique values and are of the form If fq (x) = a : : : , then a  fd (x)  b, where a; b 2 Vd are real numbers. A rough set approach to ordinal prediction has been given in [11], and a similar problem, applied to the rough approximation of a preference relation, has been studied in [20]. We also should like to mention that the rough approximation of relations is of considerable importance, for example, in social choice, preference relations, or spatial reasoning [5, 6, 8, 16, 81]. 10 Concluding remarks RSDA is the analysis of data based purely on the description of granularity expressed by equivalence relations on the object set U , where the only explicit model assumption is the operationalisation of data in form of an information system designed by a researcher. In that, RSDA differs from most other data analysis methods, and this is where its strengths, but also its weaknesses lie. The strength of requiring no outside parameters is that the method is applicable (and, indeed, applied) to all situations in which data is given in the RSDA operationalisation of information systems. In most cases, real life problems have few data points with respect to state complexity, and show many attribute dependencies (Table 6). Therefore, traditional statistical models are not always optimal tools for knowledge discovery because of their model assumption of representativeness of the sample, and their sensitivity to irrelevant features. In these cases, RSDA can serve as a useful pre-processing tool, removing 19 superfluous attributes and, with the ROUGHIAN extensions, suggesting validated rules and models on an objective and global basis, i.e without any tuning for a particular application. 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