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文章基本信息

  • 标题:Model-Based Outlier Detection System with Statistical Preprocessing
  • 本地全文:下载
  • 作者:Singh, D. Asir Antony Gnana ; Leavline, E. Jebalamar
  • 期刊名称:Journal of Modern Applied Statistical Methods
  • 出版年度:2016
  • 卷号:15
  • 期号:1
  • 页码:39
  • 出版社:Wayne State University
  • 摘要:Reliability, lack of error, and security are important improvements to quality of service. Outlier detection is a process of detecting the erroneous parts or abnormal objects in defined populations, and can contribute to secured and error-free services. Outlier detection approaches can be categorized into four types: statistic-based, unsupervised, supervised, and semi-supervised. A model-based outlier detection system with statistical preprocessing is proposed, taking advantage of the statistical approach to preprocess training data and using unsupervised learning to construct the model. The robustness of the proposed system is evaluated using the performance evaluation metrics sum of squared error (SSE) and time to build model (TBM). The proposed system performs better for detecting outliers regardless of the application domain.
  • 关键词:Outlier; Pre-processing; Inter-quartile range; Anomaly Detection
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