Artificial intelligence

Abstract: This paper presents two novel approaches to evolutionary design of the classifier ensemble. The first one presents the task of one-objective optimization of feature set partitioning together with feature weighting for the construction of the inividual classifiers. The second approach deals with multi-objective optimization of classifier ensemble design. The proposed approaches have been tested on two data sets from the machine learning repository and one real data set on transient ischemic attack. The experiments show the advantages of the feature weighting in terms of classification accuracy when dealing with multivariate data sets and the possibility in one run of multi-objective genetic algorithm to get the non-dominated ensembles of different sizes and thereby skip the tedious process of iterative search for the best ensemble of fixed size.

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