{"title":"Fuzzy decision tree and fuzzy gradual decision tree: Application to job satisfaction","authors":"C. Marsala, M. Rifqi","doi":"10.1109/FUZZ-IEEE.2017.8015740","DOIUrl":null,"url":null,"abstract":"In this paper, a comparison of the behaviour of fuzzy decision trees and gradual fuzzy decision trees is presented in a real-world application in the context of labour economics. The aim of this study is on one hand to present, in a real case, the good property of interpretability of such decision trees. On the other hand, it shows the importance to take into account a graduality relation between attributes and the class during the construction of a fuzzy decision tree. The obtained results illustrate the differences between the two types of fuzzy decision trees.","PeriodicalId":408343,"journal":{"name":"2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"598 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FUZZ-IEEE.2017.8015740","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
In this paper, a comparison of the behaviour of fuzzy decision trees and gradual fuzzy decision trees is presented in a real-world application in the context of labour economics. The aim of this study is on one hand to present, in a real case, the good property of interpretability of such decision trees. On the other hand, it shows the importance to take into account a graduality relation between attributes and the class during the construction of a fuzzy decision tree. The obtained results illustrate the differences between the two types of fuzzy decision trees.