Many-Objective Cooperative Co-evolutionary Feature Selection: A Lexicographic Approach
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Many-objective evolutionary algorithmCooperative co-evolutionary algorithmLexicographic optimizationFeature selectionWrapper approach
González J., Ortega J., Damas M., Martín-Smith P. (2019) Many-Objective Cooperative Co-evolutionary Feature Selection: A Lexicographic Approach. In: Rojas I., Joya G., Catala A. (eds) Advances in Computational Intelligence. IWANN 2019. Lecture Notes in Computer Science, vol 11507. Springer, Cham
SponsorshipProject TIN2015-67020-P (Spanish “Ministerio de Economía y Competitividad”); Project PGC2018-098813-B-C31 (Spanish “Ministerio de Ciencia, Innovación y Universidades”); European Regional Development Funds (ERDF)
This paper presents a new wrapper method able to optimize simultaneously the parameters of the classifier while the size of the subset of features that better describe the input dataset is also being minimized. The search algorithm used for this purpose is based on a co-evolutionary algorithm optimizing several objectives related with different desirable properties for the final solutions, such as its accuracy, its final number of features, and the generalization ability of the classifier. Since these objectives can be sorted according to their priorities, a lexicographic approach has been applied to handle this many-objective problem, which allows the use of a simple evolutionary algorithm to evolve each one of the different sub-populations.