A new nonlinear classifier with a penalized signed fuzzy measure using effective genetic algorithm
Department of Quantitative Health Sciences
Algorithms; Classification; Fuzzy Logic; Computational Biology
Bioinformatics | Biostatistics | Epidemiology | Health Services Research
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.
DOI of Published Version
Pattern Recognit. 2010;43(4):1393-1401. Link to article on publisher's site
Fang H, Rizzo ML, Wang H, Espy KA, Wang Z. (2010). A new nonlinear classifier with a penalized signed fuzzy measure using effective genetic algorithm. Population and Quantitative Health Sciences Publications. https://doi.org/10.1016/j.patcog.2009.10.006. Retrieved from https://escholarship.umassmed.edu/qhs_pp/866