Title

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

UMMS Affiliation

Department of Quantitative Health Sciences

Date

3-20-2010

Document Type

Article

Medical Subject Headings

Algorithms; Classification; Fuzzy Logic; Computational Biology

Disciplines

Bioinformatics | Biostatistics | Epidemiology | Health Services Research

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.

Rights and Permissions

Citation: Pattern Recognit. 2010;43(4):1393-1401. Link to article on publisher's site

Related Resources

Link to Article in PubMed