{"title":"通过自动决策树修剪改进活动识别","authors":"T. Phan","doi":"10.1145/2638728.2641310","DOIUrl":null,"url":null,"abstract":"Activity recognition enables many user-facing smartphone applications, but it may suffer from misclassifications when trained models attempt to classify previously-unseen real-world behavior. Our system mitigates this problem by first identifying spurious classifications and then automatically pruning a decision tree model to remove labels that tend to produce wrong inferences, resulting in a 10% classification improvement based on our data set.","PeriodicalId":20496,"journal":{"name":"Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication","volume":"33 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2014-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"24","resultStr":"{\"title\":\"Improving activity recognition via automatic decision tree pruning\",\"authors\":\"T. Phan\",\"doi\":\"10.1145/2638728.2641310\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Activity recognition enables many user-facing smartphone applications, but it may suffer from misclassifications when trained models attempt to classify previously-unseen real-world behavior. Our system mitigates this problem by first identifying spurious classifications and then automatically pruning a decision tree model to remove labels that tend to produce wrong inferences, resulting in a 10% classification improvement based on our data set.\",\"PeriodicalId\":20496,\"journal\":{\"name\":\"Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication\",\"volume\":\"33 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-09-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"24\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2638728.2641310\",\"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 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2638728.2641310","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Improving activity recognition via automatic decision tree pruning
Activity recognition enables many user-facing smartphone applications, but it may suffer from misclassifications when trained models attempt to classify previously-unseen real-world behavior. Our system mitigates this problem by first identifying spurious classifications and then automatically pruning a decision tree model to remove labels that tend to produce wrong inferences, resulting in a 10% classification improvement based on our data set.
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