Multiple Classifier Systems
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Recent papers in Multiple Classifier Systems
The aim of this paper is to propose a simple procedure that a priori determines a minimum number of classifiers to combine in order to obtain a prediction accuracy level similar to the one obtained with the combination of larger... more
High-rise buildings, which have become a significant part of the urban habitat, is particularly notorious for their delayed completion times. Though, there exists a plethora of studies on construction delays, the problem however is... more
Biometric authentication is a process of verifying an identity claim using a person's behavioural and physiological characteristics. Due to the vulnerability of the system to environmental noise and variation caused by the user, fusion of... more
More than a decade ago, combining multiple classifiers was proposed as a possible solution to the problems posed by the traditional pattern classification approach which involved selecting the best classifier from a set of candidates... more
Breast cancer is the disease most common malignancy affects female population and the number of affected people is the second most common leading cause of cancer deaths among all cancer types in the developing countries. Nowadays, there... more
Research in the area of human-computer interaction (HCI) increasingly addressed the aspect of integrating some type of emotional intelligence in the system. Such systems must be able to recognize, interprete and create emotions. Although,... more
In this paper, we present biometric person recognition experiments in a real-world car environment using speech, face, and driving signals. We have performed experiments on a subset of the in-car corpus collected at the Nagoya University,... more
In this thesis we describe results of research on the determination of expert weights for the adaptive combination of two or more optical character recognition engines. The research was done within the Product and Application Development... more
The use of quality measures in pattern classification has recently received a lot of attention in the areas where the deterioration of signal quality is one of the primary causes of classification errors. An example of such domain is... more
The computational genome-wide annotation of gene functions requires the prediction of hierarchically structured functional classes and can be formalized as a multiclass, multilabel, multipath hierarchical classification problem,... more
In this thesis we provide a unifying framework for two decades of work in an area of Machine Learning known as cost-sensitive Boosting algorithms. This area is concerned with the fact that most real-world prediction problems are... more
Abstract. We have previously described an incremental learning algorithm, Learn++. NC, for learning from new datasets that may include new concept classes without accessing previously seen data. We now propose an extension, Learn++. UDNC,... more
Most conventional learning algorithms require both positive
We address one of the main open issues about the use of diversity in multiple classifier systems: the effectiveness of the explicit use of diversity measures for creation of classifier ensembles. So far, diversity measures have been... more
Rapid advances in remote sensing sensor technology have made it recently possible to collect new dense 3D data like Light Detection And Ranging (LIDAR). One of the challenging issues about LIDAR data is classification of these data for... more
The concept of 'diversity' has been one of the main open issues in the field of multiple classifier systems. In this paper we address a facet of diversity related to its effectiveness for ensemble construction, namely, explicitly using... more
An ensemble of classifiers based algorithm, Learn++, was recently introduced that is capable of incrementally learning new information from datasets that consecutively become available, even if the new data introduce additional classes... more
Mammography is a not invasive diagnostic technique widely used for early detection of breast cancer. One of the main indicants of cancer is the presence of microcalcifications, i.e. small calcium accumulations, often grouped into... more
Ensemble methods with Random Oracles have been proposed recently . A random-oracle classifier consists of a pair of classifiers and a fixed, randomly created oracle that selects between them. Ensembles of random-oracle decision trees were... more
Mammography is a not invasive diagnostic technique widely used for early detection of breast cancer. One of the main indicants of cancer is the presence of microcalcifications, i.e. small calcium accumulations, often grouped into... more
Asymmetric classification problems are characterized by class imbalance or unequal costs for different types of misclassifications. One of the main cited weaknesses of AdaBoost is its perceived inability to handle asymmetric problems. As... more
The dynamical evolution of weights in the AdaBoost algorithm contains useful information about the rôle that the associated data points play in the built of the AdaBoost model. In particular, the dynamics induces a bipartition of the data... more
In this paper we examine the effect of applying ensemble learning to the performance of collaborative filtering methods. We present several systematic approaches for generating an ensemble of collaborative filtering models based on a... more
Recent findings in the domain of combining classifiers provide a surprising revision of the usefulness of diversity for modelling combined performance. Although there is a common agreement that a successful fusion system should be... more
The use of multiple features by a classifier often leads to a reduced probability of error, but the design of an optimal Bayesian classifier for multiple features is dependent on the estimation of multidimensional joint probability... more
In this paper, we present biometric person recognition experiments in a real-world car environment using speech, face, and driving signals. We have performed experiments on a subset of the in-car corpus collected at the Nagoya University,... more
The performance of neural nets can be improved through the use of ensembles of redundant nets. In this paper, some of the available methods of ensemble creation are reviewed and the “test and select” methodolology for ensemble creation is... more
Rotation Forest is a recently proposed method for building classifier ensembles using independently trained decision trees. It was found to be more accurate than bagging, AdaBoost and Random Forest ensembles across a collection of... more
In this paper we propose a strategy for constructing datadriven kernels, automatically determined by the training examples. Basically, their associated Reproducing Kernel Hilbert Spaces arise from finite sets of linearly independent... more
In this paper, we present some recent developments of Multiple Classifiers Systems (MCS) for remote sensing applications. Some standard MCS methods (boosting, bagging, consensus theory and random forests) are briefly described and applied... more