Multiobjective information theoretic ensemble selection

Stuart W. Card; Chilukuri K. Mohan

doi:10.1117/12.820745

29 April 2009 Multiobjective information theoretic ensemble selection

Stuart W. Card, Chilukuri K. Mohan

Proceedings Volume 7347, Evolutionary and Bio-Inspired Computation: Theory and Applications III; 734703 (2009) https://doi.org/10.1117/12.820745
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

Abstract

In evolutionary learning, the sine qua non is evolvability, which requires heritability of fitness and a balance between exploitation and exploration. Unfortunately, commonly used fitness measures, such as root mean squared error (RMSE), often fail to reward individuals whose presence in the population is needed to explain important data variance; and indicators of diversity generally are not only incommensurate with those of fitness but also essentially arbitrary. Thus, due to poor scaling, deception, etc., apparently relatively high fitness individuals in early generations may not contain the building blocks needed to evolve optimal solutions in later generations. To reward individuals for their potential incremental contributions to the solution of the overall problem, heritable information theoretic functionals are developed that incorporate diversity considerations into fitness, explicitly identifying building blocks suitable for recombination (e.g. for non-random mating). Algorithms for estimating these functionals from either discrete or continuous data are illustrated by application to input selection in a high dimensional industrial process control data set. Multiobjective information theoretic ensemble selection is shown to avoid some known feature selection pitfalls.

Citation Download Citation

Stuart W. Card and Chilukuri K. Mohan "Multiobjective information theoretic ensemble selection", Proc. SPIE 7347, Evolutionary and Bio-Inspired Computation: Theory and Applications III, 734703 (29 April 2009); https://doi.org/10.1117/12.820745

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