Nonlinear Classification by Genetic Algorithm with Signed Fuzzy Measure

—In this paper, we propose a new nonlinear classier based on a generalized Choquet integral with signed fuzzy measures to enhance the classification power by capturing all possible interactions among two or more attributes. A special genetic algorithm is designed to implement this classification optimization with fast convergence. Instead of using a discrete misclassification rate, the objective function to be optimized in this research is a continuous Choquet distance with a penalty coefficient for misclassified points. The numerical experiment shows that the special genetic algorithm effectively solves the nonlinear classification problem and this nonlinear classifier accurately identifies classes.

• Type: Conference
• Year: 2007
• Where: FUZZIEEE
• Authors: Honggang Wang, Hua Fang, Hamid Sharif, Zhenyuan Wang
• Keywords: Artificial Intelligence, Classification Optimization, FUZZIEEE 2007, Nonlinear Classification Problem, Special Genetic Algorithm