{"title":"新的多元决策树构造启发式方法","authors":"M. Amasyali, O. Ersoy","doi":"10.1109/CIMA.2005.1662359","DOIUrl":null,"url":null,"abstract":"Decision trees are often used in pattern recognition and regression problems. They are attractive due to high performance and easy-to-understand rules. Many different decision tree construction algorithms have been developed because of their popularity. In this work, we describe some new heuristic tree construction algorithms and test with 8 benchmark datasets. We compare the new method with other 21 tree induction algorithms. The results show that cline heuristics can be used in all types of classification problems because of its simplicity and acceptable performance","PeriodicalId":306045,"journal":{"name":"2005 ICSC Congress on Computational Intelligence Methods and Applications","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Cline: new multivariate decision tree construction heuristics\",\"authors\":\"M. Amasyali, O. Ersoy\",\"doi\":\"10.1109/CIMA.2005.1662359\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Decision trees are often used in pattern recognition and regression problems. They are attractive due to high performance and easy-to-understand rules. Many different decision tree construction algorithms have been developed because of their popularity. In this work, we describe some new heuristic tree construction algorithms and test with 8 benchmark datasets. We compare the new method with other 21 tree induction algorithms. The results show that cline heuristics can be used in all types of classification problems because of its simplicity and acceptable performance\",\"PeriodicalId\":306045,\"journal\":{\"name\":\"2005 ICSC Congress on Computational Intelligence Methods and Applications\",\"volume\":\"2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2005-12-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2005 ICSC Congress on Computational Intelligence Methods and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CIMA.2005.1662359\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2005 ICSC Congress on Computational Intelligence Methods and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIMA.2005.1662359","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Cline: new multivariate decision tree construction heuristics
Decision trees are often used in pattern recognition and regression problems. They are attractive due to high performance and easy-to-understand rules. Many different decision tree construction algorithms have been developed because of their popularity. In this work, we describe some new heuristic tree construction algorithms and test with 8 benchmark datasets. We compare the new method with other 21 tree induction algorithms. The results show that cline heuristics can be used in all types of classification problems because of its simplicity and acceptable performance