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Automatic feature selection by genetic algorithms. (English) Zbl 1011.68167
Kůrková, Věra (ed.) et al., Artificial neural nets and genetic algorithms. Proceedings of the international conference, Prague, Czech Republic, 2001. Wien: Springer. 256-259 (2001).
Summary: The efficient and automatic selection of features from an initial raw data set is an optimization task met in numerous applications fields, e.g., multivariate data classification, analysis, and visualization. The reduction of the variable number reduces the detrimental effects of the well-known curse of dimensionality. However, finding of the optimum solution in the selection process by exhaustive search is infeasible, as the underlying optimization problem is NP-complete. Thus, search heuristics are commonly applied to find acceptable solutions with a feasible computational effort. In this work, genetic algorithms are applied, based on dedicated nonparametric cost functions and multiobjective optimization. The method was implemented in our general QuickCog environment. For practical applications competitive results were achieved.
MSC:
68W05Nonnumerical algorithms
68T05Learning and adaptive systems
Keywords:
QuickCog