{"title":"自适应神经模糊推理系统、神经网络和支持向量机的呼叫者行为分类","authors":"Pretesh B. Patel, T. Marwala","doi":"10.1109/ICMLA.2011.24","DOIUrl":null,"url":null,"abstract":"A classification system that accurately categorizes caller behavior within Interactive Voice Response systems would assist in developing good automated self service applications. This paper details the implementation of such a classification system for a pay beneficiary application. Adaptive Neuro-Fuzzy Inference System (ANFIS), Feed forward Artificial Neural Network (ANN) and Support Vector Machine (SVM) classifiers were created. Exceptional results were achieved. The ANN classifiers are the preferred models. ANN classifiers achieved 100% classification on 'Say account', 'Say amount' and 'Select beneficiary' unseen data. The ANN classifier yielded 95.42% accuracy on 'Say confirmation' unseen data.","PeriodicalId":439926,"journal":{"name":"2011 10th International Conference on Machine Learning and Applications and Workshops","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Adaptive Neuro Fuzzy Inference System, Neural Network and Support Vector Machine for Caller Behavior Classification\",\"authors\":\"Pretesh B. Patel, T. Marwala\",\"doi\":\"10.1109/ICMLA.2011.24\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A classification system that accurately categorizes caller behavior within Interactive Voice Response systems would assist in developing good automated self service applications. This paper details the implementation of such a classification system for a pay beneficiary application. Adaptive Neuro-Fuzzy Inference System (ANFIS), Feed forward Artificial Neural Network (ANN) and Support Vector Machine (SVM) classifiers were created. Exceptional results were achieved. The ANN classifiers are the preferred models. ANN classifiers achieved 100% classification on 'Say account', 'Say amount' and 'Select beneficiary' unseen data. The ANN classifier yielded 95.42% accuracy on 'Say confirmation' unseen data.\",\"PeriodicalId\":439926,\"journal\":{\"name\":\"2011 10th International Conference on Machine Learning and Applications and Workshops\",\"volume\":\"24 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-12-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 10th International Conference on Machine Learning and Applications and Workshops\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMLA.2011.24\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 10th International Conference on Machine Learning and Applications and Workshops","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLA.2011.24","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Adaptive Neuro Fuzzy Inference System, Neural Network and Support Vector Machine for Caller Behavior Classification
A classification system that accurately categorizes caller behavior within Interactive Voice Response systems would assist in developing good automated self service applications. This paper details the implementation of such a classification system for a pay beneficiary application. Adaptive Neuro-Fuzzy Inference System (ANFIS), Feed forward Artificial Neural Network (ANN) and Support Vector Machine (SVM) classifiers were created. Exceptional results were achieved. The ANN classifiers are the preferred models. ANN classifiers achieved 100% classification on 'Say account', 'Say amount' and 'Select beneficiary' unseen data. The ANN classifier yielded 95.42% accuracy on 'Say confirmation' unseen data.