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Proceedings Paper

Multiobjective information theoretic ensemble selection
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Paper 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.

Paper Details

Date Published: 30 April 2009
PDF: 7 pages
Proc. SPIE 7347, Evolutionary and Bio-Inspired Computation: Theory and Applications III, 734703 (30 April 2009); doi: 10.1117/12.820745
Show Author Affiliations
Stuart W. Card, Syracuse Univ. (United States)
Critical Technologies Inc. (United States)
Chilukuri K. Mohan, Syracuse Univ. (United States)


Published in SPIE Proceedings Vol. 7347:
Evolutionary and Bio-Inspired Computation: Theory and Applications III
Teresa H. O'Donnell; Misty Blowers; Kevin L. Priddy, Editor(s)

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