Feature selection for multi-purpose predictive models: A many-objective task

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

The target of machine learning is a predictive model that performs well on unseen data. Often, such a model has multiple intended uses, related to different points in the tradeoff between (e.g.) sensitivity and specificity. Moreover, when feature selection is required, different feature subsets will suit different target performance characteristics. Given a feature selection task with such multiple distinct requirements, one is in fact faced with a very-many-objective optimization task, whose target is a Pareto surface of feature subsets, each specialized for (e.g.) a different sensitivity/specificity tradeoff profile. We argue that this view has many advantages. We motivate, develop and test such an approach. We show that it can be achieved successfully using a dominance-based multiobjective algorithm, despite an arbitrarily large number of objectives. © 2010 Springer-Verlag.

Original languageEnglish
Title of host publicationParallel Problem Solving from Nature, PPSN XI - 11th International Conference, Proceedings
Pages384-393
Number of pages10
Volume6238 LNCS
EditionPART 1
DOIs
Publication statusPublished - 2010
Event11th International Conference on Parallel Problem Solving from Nature - Krakow, Poland
Duration: 11 Sep 201015 Sep 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume6238 LNCS
ISSN (Print)0302-9743

Conference

Conference11th International Conference on Parallel Problem Solving from Nature
Abbreviated titlePPSN 2010
CountryPoland
CityKrakow
Period11/09/1015/09/10

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  • Cite this

    Reynolds, A. P., Corne, D. W., & Chantler, M. J. (2010). Feature selection for multi-purpose predictive models: A many-objective task. In Parallel Problem Solving from Nature, PPSN XI - 11th International Conference, Proceedings (PART 1 ed., Vol. 6238 LNCS, pp. 384-393). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6238 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-15844-5_39