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

Authors

Julia Hua Fang, University of NebraskaFollow
Maria L. Rizzo, Bowling Green State University,Follow
Honggang Wang, University of Massachusetts, Dartmouth,Follow
Kimberly A. Espy, University of Nebraska-LincolnFollow
Zhenyuan Wang, University of Nebraska–Omaha,Follow

Date of this Version

2010

Comments

Published in Pattern Recognition 43 (2010), pp. 1393–1401; doi: 10.1016/j.patcog.2009.10.006 Copyright © 2009 Elsevier Ltd. Used by permission. http://www.elsevier.de/locate/pr

Abstract

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.