{"title":"多标签分类:一种使用决策树学习标签关系和防止循环依赖的新方法:关系识别和去除循环(3RC)","authors":"Hamza Lotf, M. Ramdani","doi":"10.1145/3419604.3419763","DOIUrl":null,"url":null,"abstract":"Multi-Label Classification (MLC) is a field of machine learning, which consists of classifying data by assigning to each instance a set of labels instead of one. These labels or classes can have dependencies between them. Omit this information can affect the predictive quality of classification. Considering these dependencies or ignoring them, when building the classifier, each has its drawbacks. The first approach facilitates the spread of learning errors and increases complexity of the task, especially if there are cyclical relationships between classes. While the second approach can give inconsistent predictions. There are multiple approaches designed to solve multi-label classification tasks, some of them take into consideration labels dependencies and others consider them independent. A new approach called PSI-MC proposes a novel way to learn the relations between labels without fixing a predefined structure. We propose an approach that uses the same principle as the PSI- MC, and which improves the way to eliminate cycles. Finally, we present the results of testing our new approach on four different datasets. According to four measures, our proposed approach called (3RC) is much better than binary relevance, RAKEL and MLKNN approaches.","PeriodicalId":250715,"journal":{"name":"Proceedings of the 13th International Conference on Intelligent Systems: Theories and Applications","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Multi-Label Classification: A Novel approach using decision trees for learning Label-relations and preventing cyclical dependencies: Relations Recognition and Removing Cycles (3RC)\",\"authors\":\"Hamza Lotf, M. Ramdani\",\"doi\":\"10.1145/3419604.3419763\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Multi-Label Classification (MLC) is a field of machine learning, which consists of classifying data by assigning to each instance a set of labels instead of one. These labels or classes can have dependencies between them. Omit this information can affect the predictive quality of classification. Considering these dependencies or ignoring them, when building the classifier, each has its drawbacks. The first approach facilitates the spread of learning errors and increases complexity of the task, especially if there are cyclical relationships between classes. While the second approach can give inconsistent predictions. There are multiple approaches designed to solve multi-label classification tasks, some of them take into consideration labels dependencies and others consider them independent. A new approach called PSI-MC proposes a novel way to learn the relations between labels without fixing a predefined structure. We propose an approach that uses the same principle as the PSI- MC, and which improves the way to eliminate cycles. Finally, we present the results of testing our new approach on four different datasets. According to four measures, our proposed approach called (3RC) is much better than binary relevance, RAKEL and MLKNN approaches.\",\"PeriodicalId\":250715,\"journal\":{\"name\":\"Proceedings of the 13th International Conference on Intelligent Systems: Theories and Applications\",\"volume\":\"14 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-09-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 13th International Conference on Intelligent Systems: Theories and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3419604.3419763\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 13th International Conference on Intelligent Systems: Theories and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3419604.3419763","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Multi-Label Classification: A Novel approach using decision trees for learning Label-relations and preventing cyclical dependencies: Relations Recognition and Removing Cycles (3RC)
Multi-Label Classification (MLC) is a field of machine learning, which consists of classifying data by assigning to each instance a set of labels instead of one. These labels or classes can have dependencies between them. Omit this information can affect the predictive quality of classification. Considering these dependencies or ignoring them, when building the classifier, each has its drawbacks. The first approach facilitates the spread of learning errors and increases complexity of the task, especially if there are cyclical relationships between classes. While the second approach can give inconsistent predictions. There are multiple approaches designed to solve multi-label classification tasks, some of them take into consideration labels dependencies and others consider them independent. A new approach called PSI-MC proposes a novel way to learn the relations between labels without fixing a predefined structure. We propose an approach that uses the same principle as the PSI- MC, and which improves the way to eliminate cycles. Finally, we present the results of testing our new approach on four different datasets. According to four measures, our proposed approach called (3RC) is much better than binary relevance, RAKEL and MLKNN approaches.