Adaptive Neuro Fuzzy Inference System, Neural Network and Support Vector Machine for Caller Behavior Classification

Pretesh B. Patel, T. Marwala
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引用次数: 5

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.
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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
在交互式语音应答系统中准确地对呼叫者行为进行分类的分类系统将有助于开发良好的自动化自助服务应用程序。本文详细介绍了支付受益人申请的这种分类系统的实现。建立了自适应神经模糊推理系统(ANFIS)、前馈人工神经网络(ANN)和支持向量机(SVM)分类器。取得了优异的成绩。人工神经网络分类器是首选模型。人工神经网络分类器在“说账户”、“说金额”和“选择受益人”看不见的数据上实现了100%的分类。ANN分类器在“Say confirmation”未见数据上的准确率为95.42%。
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