Titlebook: Ensembles in Machine Learning Applications; Oleg Okun,Giorgio Valentini,Matteo Re Book 2011 Springer Berlin Heidelberg 2011 Computational

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On the Design of Low Redundancy Error-Correcting Output Codes, public UCI data sets and different multi-class Computer Vision problems show that the proposed methodology obtains comparable (even better) results than the state-of-the-art ECOC methodologies with far less number of dichotomizers.

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On the Design of Low Redundancy Error-Correcting Output Codes,essed using an ensemble of classifiers . In this scope, the Error-Correcting Output Codes framework has demonstrated to be a powerful tool for combining classifiers. However, most of the state-of-the-art ECOC approaches use a linear or exponential number of classifiers, making the discrimination of

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Minimally-Sized Balanced Decomposition Schemes for Multi-class Classification,lass classification problem as a set of binary classification problems. Due to code redundancy ECOC schemes can significantly improve generalization performance on multi-class classification problems. However, they can face a computational complexity problem when the number of classes is large..In t

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Bias-Variance Analysis of ECOC and Bagging Using Neural Nets,gating (Bagging) and Error Correcting Output Coding (ECOC) ensembles using a biasvariance framework; and make comparisons with single classifiers, while having Neural Networks (NNs) as base classifiers. As the performance of the ensembles depends on the individual base classifiers, it is important t

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Hybrid Correlation and Causal Feature Selection for Ensemble Classifiers,lgorithms cannot scale up to deal with high dimensional data, that is more than few hundred features. This chapter presents hybrid correlation and causal feature selection for ensemble classifiers to deal with this problem. Redundant features are removed by correlation-based feature selection and th

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Learning Markov Blankets for Continuous or Discrete Networks via Feature Selection,ensemble masking measures can provide an approximate Markov Blanket. Consequently, an ensemble feature selection method can be used to learnMarkov Blankets for either discrete or continuous networks (without linear, Gaussian assumptions). We use masking measures for redundancy and statistical infere

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Ensembles of Bayesian Network Classifiers Using Glaucoma Data and Expertise,tion of glaucoma, a major cause of blindness worldwide. We use visual field and retinal data to predict the early onset of glaucoma. In particular, the ability of BNs to deal with missing data allows us to select an optimal data-driven network by comparing supervised and semi-supervised models. An e