呼叫中心数据异常检测的特征选择

Leonardo O. Iheme, Ş. Ozan
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引用次数: 1

摘要

在这项研究中,我们提出了设计用于检测呼叫中心座席不当行为的机器学习模型的过程。基于从给定电话会话的录音中提取的特征,训练、评估和比较适当的一类支持向量机、隔离森林和多变量高斯模型,以确定最佳用例。实验中使用的标记数据来自真实的呼叫中心,结果表明该系统可用于真实场景。使用F1分数作为度量来验证使用的机器学习模型的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Feature Selection for Anomaly Detection in Call Center Data
In this study, we present the process of designing machine learning models for the detection of call center agent malpractices. Based on the features extracted from audio recordings of a given telephone conversation, appropriate one-class support vector machine, isolation forest, and multivariate Gaussian models are trained, evaluated and compared in order to determine the best use case. The labeled data used in the experiments was obtained from a real call center and the results obtained indicate that the system is usable in a real-world scenario. The accuracy of used machine learning models are validated by using the F1 score as a metric.
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