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A New Probabilistic Induction Method

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Abstract

Knowledge acquisition by interviewing a domain expert is one of the most problematic aspects of the development of expert systems. As an alternative, methods for inducing concept descriptions from examples have proven useful in eliminating this bottleneck. In this paper, we propose a probabilistic induction method (PIM), which is an improvement of the Chan and Wong method, for detecting relevant patterns implicit in a given data set. PIM uses the technique of residual analysis and several heuristics to effectively detect complex relevant patterns and to avoid the problem of combinatorial explosion. A reasonable trade-off between the induction time and the classification ratio is achieved. Moreover, PIM quickly classifies unknown objects using classification rules converted from the positively relevant patterns detected. Three experiments are conducted to confirm the validity of PIM.

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Authors and Affiliations

  1. Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan, R.O.C.

    Rong-Huei Hou

  2. Department of Information Management, Kaohsiung Polytechnic Institute, Kaohsiung, Taiwan, R.O.C.

    Tzung-Pei Hong

  3. Department of Computer and Information Science, National Chiao-Tung University, Hsin-Chu, Taiwan, R.O.C.

    Shian-Shyong Tseng & Sy-Yen Kuo

Authors
  1. Rong-Huei Hou
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  2. Tzung-Pei Hong
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  3. Shian-Shyong Tseng
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  4. Sy-Yen Kuo
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Hou, RH., Hong, TP., Tseng, SS. et al. A New Probabilistic Induction Method. Journal of Automated Reasoning 18, 5–24 (1997). https://doi.org/10.1023/A:1005726727996

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