{"title":"基于模糊 $$beta$ 覆盖和模糊证据理论的混合信息系统特征选择","authors":"Xiaoqin Ma, Huanhuan Hu, Qinli Zhang, Yi Xu","doi":"10.1007/s40747-024-01560-7","DOIUrl":null,"url":null,"abstract":"<p>Feature selection plays a crucial role in machine learning, as it eliminates data noise and redundancy, thereby significantly reducing computational complexity and enhancing the overall performance of the model. The challenges of feature selection for hybrid information systems stem from the difficulty in quantifying the disparities among nominal attribute values. Furthermore, a significant majority of the current methodologies exhibit sensitivity to noise. This paper introduces techniques that address the aforementioned issues from the perspective of fuzzy evidence theory. First of all, a new distance incorporating decision attributes is defined, and then a relation between fuzzy evidence theory and fuzzy <span>\\(\\beta \\)</span> covering with an anti-noise mechanism is established. In this framework, two robust feature selection algorithms for hybrid data are proposed based on fuzzy belief and fuzzy plausibility. Experiments on 10 data sets of various types show that compared with the other 6 state-of-the-art algorithms, the proposed algorithms improve the anti-noise ability by at least 6% with higher average classification accuracy. Therefore, it can be concluded that the proposed algorithms have excellent anti-noise ability while maintaining good feature selection ability.</p>","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":5.0000,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Feature selection for hybrid information systems based on fuzzy $$\\\\beta $$ covering and fuzzy evidence theory\",\"authors\":\"Xiaoqin Ma, Huanhuan Hu, Qinli Zhang, Yi Xu\",\"doi\":\"10.1007/s40747-024-01560-7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Feature selection plays a crucial role in machine learning, as it eliminates data noise and redundancy, thereby significantly reducing computational complexity and enhancing the overall performance of the model. The challenges of feature selection for hybrid information systems stem from the difficulty in quantifying the disparities among nominal attribute values. Furthermore, a significant majority of the current methodologies exhibit sensitivity to noise. This paper introduces techniques that address the aforementioned issues from the perspective of fuzzy evidence theory. First of all, a new distance incorporating decision attributes is defined, and then a relation between fuzzy evidence theory and fuzzy <span>\\\\(\\\\beta \\\\)</span> covering with an anti-noise mechanism is established. In this framework, two robust feature selection algorithms for hybrid data are proposed based on fuzzy belief and fuzzy plausibility. Experiments on 10 data sets of various types show that compared with the other 6 state-of-the-art algorithms, the proposed algorithms improve the anti-noise ability by at least 6% with higher average classification accuracy. Therefore, it can be concluded that the proposed algorithms have excellent anti-noise ability while maintaining good feature selection ability.</p>\",\"PeriodicalId\":10524,\"journal\":{\"name\":\"Complex & Intelligent Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.0000,\"publicationDate\":\"2024-07-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Complex & Intelligent Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1007/s40747-024-01560-7\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Complex & Intelligent Systems","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s40747-024-01560-7","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Feature selection for hybrid information systems based on fuzzy $$\beta $$ covering and fuzzy evidence theory
Feature selection plays a crucial role in machine learning, as it eliminates data noise and redundancy, thereby significantly reducing computational complexity and enhancing the overall performance of the model. The challenges of feature selection for hybrid information systems stem from the difficulty in quantifying the disparities among nominal attribute values. Furthermore, a significant majority of the current methodologies exhibit sensitivity to noise. This paper introduces techniques that address the aforementioned issues from the perspective of fuzzy evidence theory. First of all, a new distance incorporating decision attributes is defined, and then a relation between fuzzy evidence theory and fuzzy \(\beta \) covering with an anti-noise mechanism is established. In this framework, two robust feature selection algorithms for hybrid data are proposed based on fuzzy belief and fuzzy plausibility. Experiments on 10 data sets of various types show that compared with the other 6 state-of-the-art algorithms, the proposed algorithms improve the anti-noise ability by at least 6% with higher average classification accuracy. Therefore, it can be concluded that the proposed algorithms have excellent anti-noise ability while maintaining good feature selection ability.
期刊介绍:
Complex & Intelligent Systems aims to provide a forum for presenting and discussing novel approaches, tools and techniques meant for attaining a cross-fertilization between the broad fields of complex systems, computational simulation, and intelligent analytics and visualization. The transdisciplinary research that the journal focuses on will expand the boundaries of our understanding by investigating the principles and processes that underlie many of the most profound problems facing society today.