Paper Info
Title
A new nonlinear classifier with a penalized signed fuzzy measure using effective genetic algorithm.
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.
Year
DOI
Venue
2010
10.1016/j.patcog.2009.10.006
Pattern Recognition
Keywords
Field
DocType
choquet nonlinear classification,automatic search,new nonlinear classifier,choquet-integral classification issue,generalized approach,fuzzy measure,classification optimization,empirical case study,classification accuracy,effective genetic algorithm,choquet distance,fast convergence,genetic algorithm,optimization,bioinformatics,choquet integral,classification,primary,biomedical research
Convergence (routing),Data mining,Nonlinear system,Rate of convergence,Artificial intelligence,Choquet integral,Classifier (linguistics),Genetic algorithm,Pattern recognition,Fuzzy measure theory,Fuzzy logic,Machine learning,Mathematics
Journal
Volume
Issue
ISSN
43
4
0031-3203
Citations 
PageRank 
References 
13
0.92
26
Authors
5
Name
Order
Citations
PageRank
Hua Fang134332.48
Maria L. Rizzo21079.54
Honggang Wang31365124.06
Kimberly Andrews Espy4221.94
Zhenyuan Wang568490.22