Papers by Bernard Manderick
Springer eBooks, Sep 9, 2008
Lecture notes in networks and systems, Aug 20, 2017
Machine learning for data mining applications in the field of bioinformatics is to extract new kn... more Machine learning for data mining applications in the field of bioinformatics is to extract new knowledge to provide an improved and effective diagnosis process for patients. In this paper, we introduce an adaptive ensemble learning for classifying high-dimensional multi-class imbalanced genomic data. The aspect is to design and develop an optimal ensemble method for information discovery on genomic data, which improve the prediction accuracy of DNA variant classification. The proposed method is based on ensemble of decision trees, data pre-processing, feature selection and grouping. It converts an imbalanced genomic data into multiple balanced ones and then builds a number of decision trees on these multiple data with specific feature groups. The outputs of these trees are combined for classifying new instances by majority voting technique. In this empirical study, different ensemble predictive modelling techniques like Random Forest, Boosting and Bagging were compared with the proposed ensemble method. The experimental results on genomic data (148 Exome datasets) of Brugada syndrome from the Centre of Medical Genetics, VUB UZ Brussel show that the proposed method is usually superior to the conventional ensemble learning algorithms when classifying the high-dimensional multi-class imbalanced genomic data.
High-dimensional genomic big data with hundred of features present a big challenge in cluster ana... more High-dimensional genomic big data with hundred of features present a big challenge in cluster analysis. Usually, genomic data are noisy and have correlation among the features. Also, different subspaces exist in high-dimensional genomic data. This paper presents a feature selecting and grouping method for ensemble clustering of high-dimensional genomic data. Two most popular clustering methods: k-means and similarity-based clustering are used for ensemble clustering. Ensemble clustering is more effective in clustering high-dimensional complex data than the traditional clustering algorithms. In this paper, we cluster un-labeled genomic data (148 Exome data sets) of Brugada syndrome from the Centre of Medical Genetics, VUB UZ Brussel using SimpleKMeans, XMeans, DBScan, and MakeDensityBasedCluster algorithms and compare the clustering results with proposed ensemble clustering method. Furthermore, we use biclustering (δ-Biclustering) algorithm on each cluster to find the sub-matrices in the genomic data, which clusters both instances and features simultaneously.
Lecture Notes in Computer Science, 2015
The multi-objective, multi-armed bandits (MOMABs) problem is a Markov decision process with stoch... more The multi-objective, multi-armed bandits (MOMABs) problem is a Markov decision process with stochastic rewards. Each arm generates a vector of rewards instead of a single reward and these multiple rewards might be conflicting. The agent has a set of optimal arms and the agent's goal is not only finding the optimal arms, but also playing them fairly. To find the optimal arm set, the agent uses a linear scalarized (LS) function which converts the multi-objective arms into one-objective arms. LS function is simple, however it can not find all the optimal arm set. As a result, we extend knowledge gradient (KG) policy to LS function. We propose two variants of linear scalarized-KG, LS-KG across arms and dimensions. We experimentally compare the two variant, LS-KG across arms finds the optimal arm set, while LS-KG across dimensions plays fairly the optimal arms.
Springer eBooks, 2007
Several paradigms exist for modeling causal graphical models for discrete variables that can hand... more Several paradigms exist for modeling causal graphical models for discrete variables that can handle latent variables without explicitly modeling them quantitatively. Applying them to a problem domain consists of different steps: structure learning, parameter learning and using them for probabilistic or causal inference. We discuss two well-known formalisms, namely semi-Markovian causal models and maximal ancestral graphs and indicate their strengths and limitations. Previously an algorithm has been constructed that by combining elements from both techniques allows to learn a semi-Markovian causal models from a mixture of observational and experimental data. The goal of this paper is to recapitulate the integral learning process from observational and experimental data and to demonstrate how different types of inference can be performed efficiently in the learned models. We will do this by proposing an alternative representation for semi-Markovian causal models.
Expert Systems With Applications, Dec 1, 2016
In this paper, we introduce a new adaptive rule-based classifier for multi-class classification o... more In this paper, we introduce a new adaptive rule-based classifier for multi-class classification of biological data, where several problems of classifying biological data are addressed: overfitting, noisy instances and class-imbalance data. It is well known that rules are interesting way for representing data in a human interpretable way. The proposed rule-based classifier combines the random subspace and boosting approaches with ensemble of decision trees to construct a set of classification rules without involving global optimisation. The classifier considers random subspace approach to avoid overfitting, boosting approach for classifying noisy instances and ensemble of decision trees to deal with class-imbalance problem. The classifier uses two popular classification techniques: decision tree and k-nearest-neighbor algorithms. Decision trees are used for evolving classification rules from the training data, while k-nearest-neighbor is used for analysing the misclassified instances and removing vagueness between the contradictory rules. It considers a series of k iterations to develop a set of classification rules from the training data and pays more attention to the misclassified instances in the next iteration by giving it a boosting flavour. This paper particularly focuses to come up with an optimal ensemble classifier that will help for improving the prediction accuracy of DNA variant identification and classification task. The performance of proposed classifier is tested with compared to well-approved existing machine learning and data mining algorithms on genomic data (148 Exome data sets) of Brugada syndrome and 10 real benchmark life sciences data sets from the UCI (University of California, Irvine) machine learning repository. The experimental results indicate that the proposed classifier has exemplary classification accuracy on different types of biological data. Overall, the proposed classifier offers good prediction accuracy to new DNA variants classification where noisy and misclassified variants are optimised to increase test performance.
The European Symposium on Artificial Neural Networks, 2017
We compare three adaptive MCMC samplers to MetropolisHastings algorithm with optimal proposal dis... more We compare three adaptive MCMC samplers to MetropolisHastings algorithm with optimal proposal distribution as our benchmark. We transform a simple Evolution Strategy algorithm into a sampler and show that it already outperforms the other samplers on the test suite used in the initial research on adaptive MCMC.
info:eu-repo/semantics/publishe
Lewis signaling games are a standard model to study the emergence of language. We introduce win-s... more Lewis signaling games are a standard model to study the emergence of language. We introduce win-stay/lose-inaction, a random process that only updates behavior on success and never deviates from what was once successful, prove that it always ends up in a state of optimal communication in all Lewis signaling games, and predict the number of interactions it needs to do so: N 3 interactions for Lewis signaling games with N equiprobable types. We show three reinforcement learning algorithms (Roth-Erev learning, Q-learning, and Learning Automata) that can imitate win-stay/lose-inaction and can even cope with errors in Lewis signaling games.
The European Symposium on Artificial Neural Networks, 2015
An empirical comparative study is made of a sample of action selection policies on a test suite o... more An empirical comparative study is made of a sample of action selection policies on a test suite of the Bernoulli multi-armed bandit with K = 10, K = 20 and K = 50 arms, each for which we consider several success probabilities. For such problems the rewards are either Success or Failure with unknown success rate. Our study focusses on - greedy, UCB1-Tuned, Thompson sampling, the Gittin's index policy, the knowledge gradient and a new hybrid algorithm. The last two are not well- known in computer science. In this paper, we examine policy dependence on the horizon and report results which suggest that a new hybridized procedure based on Thompsons sampling improves on its regret.
Parallel Problem Solving from Nature, 1992
We have used the metaphor of ant colonies to define "the Ant system", a class of distributed algo... more We have used the metaphor of ant colonies to define "the Ant system", a class of distributed algorithms for combinatorial optimization. To test the Ant system we used the travelling salesman problem. In this paper we analyze some properties of Ant-cycle, the up to now best performing of the ant algorithms we have tested. We report many results regarding its performance when varying the values of control parameters and we compare it with some TSP specialized algorithms.
Lecture notes in networks and systems, Aug 20, 2017
, with details of the nature of the infringement. We will investigate the claim and if justified,... more , with details of the nature of the infringement. We will investigate the claim and if justified, we will take the appropriate steps.
Taylor & Francis eBooks, 1997
ABSTRACT We define shortest path Gaussian kernels basis functions over state graphs and state-act... more ABSTRACT We define shortest path Gaussian kernels basis functions over state graphs and state-action graphs. We empirically demonstrate that these new basis functions used in linear parametric function approximation outperform basis functions defined on the state space, the state graph and the state-action graph.
International Journal of Data Mining and Bioinformatics, 2015
Interaction Article Classification (IAC) is a specific text classification application in biologi... more Interaction Article Classification (IAC) is a specific text classification application in biological domain that tries to find out which articles describe Protein-Protein Interactions (PPIs) to help extract PPIs from biological literature more efficiently. However, the existing text representation and feature weighting schemes commonly used for text classification are not well suited for IAC. We capture and utilise biological domain knowledge, i.e. gene mentions also known as protein or gene names in the articles, to address the problem. We put forward a new gene mention order-based approach that highlights the important role of gene mentions to represent the texts. Furthermore, we also incorporate the information concerning gene mentions into a novel feature weighting scheme called Gene Mention-based Term Frequency (GMTF). By conducting experiments, we show that using the proposed representation and weighting schemes, our Interaction Article Classifier (IACer) performs better than other leading systems for the moment.
Springer eBooks, 2008
In this paper, we propose two named entity recognition systems for biomedical literature, System1... more In this paper, we propose two named entity recognition systems for biomedical literature, System1 using support vector machines and System2 using conditional random fields. Through employing several sets of experiments, we make a comprehensive comparison between these two systems. The final results reflect that System2 can achieve higher accuracy than System1, because System2 can catch more essential properties by handling the richer set of features, i.e., adding not only the individual and dynamic features as System1 does but also the combinational features, which can improve the performance further. Furthermore, with carefully designed features, System2 can recognize named entities in biomedical literature with fairly high accuracy, which can achieve the precision of 89.43%, recall of 83.32% and balanced F β=1 score of 86.28%.
Finds and Results from the Swedish Cyprus Expedition: A Gender Perspective at the Medelhavsmuseet, 2003
We introduce a new kernel for Support Vector Machine learning in a natural language setting. As a... more We introduce a new kernel for Support Vector Machine learning in a natural language setting. As a case study to incorporate domain knowledge into a kernel, we consider the problem of resolving Prepositional Phrase attachment ambiguities. The new kernel is derived from a distance function that proved to be succesful in memory-based learning. We start with the Simple Overlap Metric from which we derive a Simple Overlap Kernel and extend it with Information Gain Weighting. Finally, we combine it with a polynomial kernel to increase the dimensionality of the feature space. The closure properties of kernels guarantee that the result is again a kernel. This kernel achieves high classification accuracy and is efficient in both time and space usage. We compare our results with those obtained by memory-based and other learning methods. They make clear that the proposed kernel achieves a higher classification accuracy.
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Papers by Bernard Manderick