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2018 | OriginalPaper | Buchkapitel

Obtaining Pareto Front in Instance Selection with Ensembles and Populations

verfasst von : Mirosław Kordos, Marcin Wydrzyński, Krystian Łapa

Erschienen in: Artificial Intelligence and Soft Computing

Verlag: Springer International Publishing

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Abstract

Collective computational intelligence can be used in several ways, for example as taking the decision together by some form of a bagging ensemble or as finding the solutions by multi-objective evolutionary algorithms. In this paper we examine and compare the application of the two approaches to instance selection for creating the Pareto front of the selected subsets, where the two objectives are classification accuracy and data size reduction. As the bagging ensemble members we use DROP5 algorithms. The evolutionary algorithm is based on NSGA-II. The findings are that the evolutionary approach is faster (contrary to the popular belief) and usually provides better quality solutions, with some exceptions, were the outcome of the DROP5 ensemble is better.

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Vorheriges Kapitel Evolutionary Design and Training of Artificial Neural Networks

Nächstes Kapitel Negative Space-Based Population Initialization Algorithm (NSPIA)

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Titel: Obtaining Pareto Front in Instance Selection with Ensembles and Populations
verfasst von: Mirosław Kordos
Marcin Wydrzyński
Krystian Łapa
Verlag: Springer International Publishing
Buch: Artificial Intelligence and Soft Computing
Print ISBN: 978-3-319-91252-3

Electronic ISBN: 978-3-319-91253-0

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-91253-0_41

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