Optimized and improved fuzzy classification model construction method based on nondominated sorting genetic algorithm II (NSGA- II)

Abstract

The invention discloses an optimized and improved fuzzy classification model construction method based on a nondominated sorting genetic algorithm II (NSGA-II). The optimized and improved fuzzy classification model construction method based on the NAGA-II reduces characteristic variable, a fuzzy rule and redundancy of antecedent of the fuzzy rule, and improves explanatory of a fuzzy classification model. The optimized and improved fuzzy classification model construction method based on the NSGA-II comprises the following steps: firstly an initial decision tree is constructed by a C4.5 algorithm, the characteristic variable and fuzzy set numbers are selected; then a triangle subordinate function is utilized to convert the decision tree into the fuzzy classification model; and finally based on a NSGA-II optimized fuzzy classification model, the redundancy of the fuzzy rule is simultaneously deleted by selection of the fuzzy rule and the antecedent of the fuzzy rule, thereby improving accuracy and explanatory of the fuzzy classification model.

Description

technical field [0001] The invention belongs to the technical field of data mining and artificial intelligence, and relates to a method for constructing a fuzzy classification model, especially a method based on the optimization and improvement of the second-generation non-dominated sorting genetic algorithm (Non-dominated sorting genetic algorithm II, NSGA-II). Fuzzy classification model construction method. Background technique [0002] The knowledge expression form and reasoning mechanism of the fuzzy classification model conform to human thinking habits, and its structure and fuzzy set membership function parameters have obvious physical meaning. People can gain insight into the internal operating mechanism of classification models through easy-to-understand fuzzy rules, that is, interpretability is the most prominent feature of fuzzy classification models, especially in fields such as medicine and finance, where interpretability has even become the primary goal when con...

AI Technical Summary

Problems solved by technology

"A. Gonzalez, R. Perez. Selection of relevant features in a fuzzy genetic learning algorithm. IEEE Transactions on Systems, Man and Cybernetics. 2001(31): 417-425" Using a binary-coded genetic algorithm as input to a classification model Selection of variables and optimization of rules, but optimization of antecedents of rules is not involved

“F. Berlanga, M. Jesus, F. Herrera. Learning fuzzy rules using genetic programming: context-free grammar definition for high-dimensionality problems. Proceedings of the I Workshop on Genetic Fuzzy Systems. 2005: 136-141” and “F . Berlanga, M. Jesus, F. Herrera. GP-COACH: genetic programming-based learning of compact and accurate fuzzy rule-based classification systems for high-dimensional problems. Information Sciences. 2010(180): 1183-1200” will analyze The general formula is used in the genetic optimization of the classification model rule base, which reduces the number of fuzzy rules, but the number of rules is still large

[0005] The above technologies have optimized the fuzzy classification model and improved the interpretability of the model to varying degrees, but the redundancy of feature variables, fuzzy rules and their antecedents still exists.

Images