A new nonlinear classifier with a penalized signed fuzzy measure using effective genetic algorithm

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摘要

This paper proposes a new nonlinear classifier based on a generalized Choquet integral with signed fuzzy measures to enhance the classification accuracy and power by capturing all possible interactions among two or more attributes. This generalized approach was developed to address unsolved Choquet-integral classification issues such as allowing for flexible location of projection lines in n-dimensional space, automatic search for the least misclassification rate based on Choquet distance, and penalty on misclassified points. A special genetic algorithm is designed to implement this classification optimization with fast convergence. Both the numerical experiment and empirical case studies show that this generalized approach improves and extends the functionality of this Choquet nonlinear classification in more real-world multi-class multi-dimensional situations.

论文关键词:Choquet integral,Signed fuzzy measure,Classification,Optimization,Genetic algorithm,primary,28E10,secondary,74P99

论文评审过程:Received 18 January 2007, Revised 9 October 2009, Accepted 12 October 2009, Available online 16 October 2009.

论文官网地址:https://doi.org/10.1016/j.patcog.2009.10.006