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

PATTERN RECOGNITION

卷:43

Article

Fang, Hua²  Rizzo, Maria L.¹  Wang, Honggang³  Espy, Kimberly Andrews²  Wang, Zhenyuan⁴ 

[1] Bowling Green State Univ, Dept Math & Stat, Bowling Green, OH 43403 USA

[2] Univ Nebraska, Res Off, Lincoln, NE 68588 USA

[3] Univ Massachusetts, Dept Elect & Comp Engn, Dartmouth, MA 02747 USA

[4] Univ Nebraska, Dept Math, Omaha, NE 68182 USA

关键词: Choquet integral;    Signed fuzzy measure;    Classification;    Optimization;    Genetic algorithm;   

DOI  :  10.1016/j.patcog.2009.10.006

来源: Elsevier

【 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. (C) 2009 Elsevier Ltd. All rights reserved.

【 授权许可】

Free