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Research on case reasoning method based on TF-IDF

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  • Lin Zhang

    (AnHui SanLian University)

Abstract

With the continuous expansion of the application field of Case-Based Reasoning (CBR) technology, it is increasingly difficult for programmers to acquire and express professional knowledge. Therefore, this paper first gives a structured expression of professional knowledge, and combines the Case-Based Reasoning method with the scientific measurement of keyword weight (Term Frequency-Inverse Document Frequency, TF-IDF). The case organization, case retrieval and case retention techniques in CBR technology were designed. And on the basis of the initial design, the inter-class dispersion calculation is added to the feature word distribution to describe the distribution of feature attributes in different categories of cases. It provides an effective method for the establishment of case reasoning model. This method is a general algorithm and can be used in many industries and fields. Taking the case of traffic congestion in urban areas as an example, the source of the management decision data of traffic congestion in urban areas has the characteristics of diversity and heterogeneity. A structured expression method and a design of case retrieval technique are presented by using the algorithm in this paper. A decision support system V1 .0 for urban road congestion was developed, and software copyright authorization was obtained.

Suggested Citation

  • Lin Zhang, 2021. "Research on case reasoning method based on TF-IDF," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(3), pages 608-615, June.
    • Handle: RePEc:spr:ijsaem:v:12:y:2021:i:3:d:10.1007_s13198-021-01135-6
    DOI: 10.1007/s13198-021-01135-6
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    Cited by:

    1. Xue, Zhebin & Li, Qing & Zeng, Xianyi, 2023. "Social media user behavior analysis applied to the fashion and apparel industry in the big data era," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    2. Song Liu & Xiaoyan Ma, 2021. "How Social Networks Affect the Spatiotemporal Planning of Smart Tourism: Evidence from Shanghai," Sustainability, MDPI, vol. 13(13), pages 1-18, July.

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