{"title":"使用排序属性进行高效连接系统开发的实验","authors":"H. Ferrá, A. Kowalczyk, A. Jennings","doi":"10.1109/IJCNN.1991.170317","DOIUrl":null,"url":null,"abstract":"The authors introduce an algorithm for selection and ordering of input attributes based on a generalization to a fuzzy case of the notion of conditional entropy. The algorithm is relatively computationally inexpensive and efficient, as was demonstrated in a number of experiments that are reported. The experimental results support the observation that preselection and ordering of a small number of effective input features constitute an important factor in the development of efficient neural network classifiers.<<ETX>>","PeriodicalId":211135,"journal":{"name":"[Proceedings] 1991 IEEE International Joint Conference on Neural Networks","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"1991-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Experiments with ordering attributes for efficient connectionist system development\",\"authors\":\"H. Ferrá, A. Kowalczyk, A. Jennings\",\"doi\":\"10.1109/IJCNN.1991.170317\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The authors introduce an algorithm for selection and ordering of input attributes based on a generalization to a fuzzy case of the notion of conditional entropy. The algorithm is relatively computationally inexpensive and efficient, as was demonstrated in a number of experiments that are reported. The experimental results support the observation that preselection and ordering of a small number of effective input features constitute an important factor in the development of efficient neural network classifiers.<<ETX>>\",\"PeriodicalId\":211135,\"journal\":{\"name\":\"[Proceedings] 1991 IEEE International Joint Conference on Neural Networks\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1991-11-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"[Proceedings] 1991 IEEE International Joint Conference on Neural Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IJCNN.1991.170317\",\"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] 1991 IEEE International Joint Conference on Neural Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IJCNN.1991.170317","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Experiments with ordering attributes for efficient connectionist system development
The authors introduce an algorithm for selection and ordering of input attributes based on a generalization to a fuzzy case of the notion of conditional entropy. The algorithm is relatively computationally inexpensive and efficient, as was demonstrated in a number of experiments that are reported. The experimental results support the observation that preselection and ordering of a small number of effective input features constitute an important factor in the development of efficient neural network classifiers.<>