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

Authors

Julia Hua Fang, University of Nebraska
Maria L. Rizzo, Bowling Green State University
Honggang Wang, University of Massachusetts - Dartmouth
Kimberly Espy, University of Nebraska
Zhenyuan Wang, University of Nebraska at Omaha

Document Type

Article

Publication Date

2010

Publication Title

Pattern Recognition

Volume

43

Issue

4

First Page

1393

Last Page

1401

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.

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

Recommended Citation

Fang, Julia Hua; Rizzo, Maria L.; Wang, Honggang; Espy, Kimberly; and Wang, Zhenyuan, "A new nonlinear classifier with a penalized signed fuzzy measure using effective genetic algorithm" (2010). Mathematics Faculty Publications. 1.
https://digitalcommons.unomaha.edu/mathfacpub/1