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A time-interval sequence classification method

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Abstract

Classification is one of the most popular behavior prediction tools in behavior informatics (behavior computing) to predict group membership for data instances. It has been greatly used to support customer relationship management (CRM) such as customer identification, one-to-one marketing, fraud detection, and lifetime value analysis. Although previous studies showed themselves efficient and accurate in certain CRM classification applications, most of them took demographic, RFM-type, or activity attributes as classification criteria and seldom took temporal relationship among these attributes into account. To bridge this gap, this study takes customer temporal behavior data, called time-interval sequences, as classification criteria and develops a two-stage classification framework. In the first stage, time-interval sequential patterns are discovered from customer temporal databases. Then, a time-interval sequence classifier optimized by the particle swam optimization (PSO) algorithm is developed to achieve high classification accuracy in the second stage. The experiment results indicate the proposed time-interval sequence classification framework is efficient and accurate to predict the class label of new customer temporal data.

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Authors and Affiliations

  1. Department of Industrial Engineering and Management, Yuan Ze University, No. 135, Yuantung Rd., Chungli City, Taoyuan County, 320, Taiwan

    Chieh-Yuan Tsai & Chih-Jung Chen

  2. Department of Supplier Quality Engineering, Showa Denko HD Trace Corp., Hsinchu, Taiwan

    Chun-Ju Chien

Authors
  1. Chieh-Yuan Tsai
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  2. Chih-Jung Chen
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  3. Chun-Ju Chien
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Correspondence to Chieh-Yuan Tsai.

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Tsai, CY., Chen, CJ. & Chien, CJ. A time-interval sequence classification method. Knowl Inf Syst 37, 251–278 (2013). https://doi.org/10.1007/s10115-012-0501-1

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  • DOI: https://doi.org/10.1007/s10115-012-0501-1

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