基于模糊规则的套袋多分类系统遗传选择精度与多样性相结合的研究

2009 Ninth International Conference on Intelligent Systems Design and Applications Pub Date : 2009-11-30 DOI:10.1109/ISDA.2009.123

Krzysztof Trawiński, A. Quirin, O. Cordón

{"title":"基于模糊规则的套袋多分类系统遗传选择精度与多样性相结合的研究","authors":"Krzysztof Trawiński, A. Quirin, O. Cordón","doi":"10.1109/ISDA.2009.123","DOIUrl":null,"url":null,"abstract":"A preliminary study combining two diversity measures with an accuracy measure in two bicriteria fitness functions to genetically select fuzzy rule-based multiclassification systems is conducted in this paper. The fuzzy rule-based classification system ensembles are generated by means of bagging and mutual information-based feature selection. Several experiments were developed using four popular UCI datasets with different dimensionality in order to analyze the accuracy-complexity trade-off obtained by a genetic algorithm considering the two fitness functions. Comparison are made with the initial fuzzy ensemble and a single fuzzy classifier.","PeriodicalId":330324,"journal":{"name":"2009 Ninth International Conference on Intelligent Systems Design and Applications","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"On the Combination of Accuracy and Diversity Measures for Genetic Selection of Bagging Fuzzy Rule-Based Multiclassification Systems\",\"authors\":\"Krzysztof Trawiński, A. Quirin, O. Cordón\",\"doi\":\"10.1109/ISDA.2009.123\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A preliminary study combining two diversity measures with an accuracy measure in two bicriteria fitness functions to genetically select fuzzy rule-based multiclassification systems is conducted in this paper. The fuzzy rule-based classification system ensembles are generated by means of bagging and mutual information-based feature selection. Several experiments were developed using four popular UCI datasets with different dimensionality in order to analyze the accuracy-complexity trade-off obtained by a genetic algorithm considering the two fitness functions. Comparison are made with the initial fuzzy ensemble and a single fuzzy classifier.\",\"PeriodicalId\":330324,\"journal\":{\"name\":\"2009 Ninth International Conference on Intelligent Systems Design and Applications\",\"volume\":\"14 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-11-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 Ninth International Conference on Intelligent Systems Design and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISDA.2009.123\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 Ninth International Conference on Intelligent Systems Design and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISDA.2009.123","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 8

摘要

本文对基于模糊规则的多分类系统的遗传选择进行了初步的研究，并结合两个双准则适应度函数中的两个多样性度量和一个准确度度量。采用套袋和互信息特征选择的方法生成基于模糊规则的分类系统集成。利用四种不同维数的常用UCI数据集进行了实验，分析了考虑两种适应度函数的遗传算法的精度-复杂度权衡。并与初始模糊集成和单一模糊分类器进行了比较。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

On the Combination of Accuracy and Diversity Measures for Genetic Selection of Bagging Fuzzy Rule-Based Multiclassification Systems

A preliminary study combining two diversity measures with an accuracy measure in two bicriteria fitness functions to genetically select fuzzy rule-based multiclassification systems is conducted in this paper. The fuzzy rule-based classification system ensembles are generated by means of bagging and mutual information-based feature selection. Several experiments were developed using four popular UCI datasets with different dimensionality in order to analyze the accuracy-complexity trade-off obtained by a genetic algorithm considering the two fitness functions. Comparison are made with the initial fuzzy ensemble and a single fuzzy classifier.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

2009 Ninth International Conference on Intelligent Systems Design and Applications

自引率

0.00%

发文量