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Rule-based approximate reasoning systems are an important decision-making tool in many application problems. The use of expert knowledge or machine learning techniques to create rules does not exhaust the problems of representing data and decision dependencies, therefore we propose a hybrid/mixed technique for creating a set of rules while effectively modeling uncertainty through interval-valued fuzzy representation in the problem of detecting falls of elderly people. The obtained prediction confirms the correctness of the choice of diagnostic methodology.

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The Hybrid Method of Inference System Based on Experts’ Rules and Machine Learning with an Uncertainty Aspect

  • 会议论文

  • 发表日期：

    2025年

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  • Dorota Gil,
  • Barbara Pękala

摘要

Rule-based approximate reasoning systems are an important decision-making tool in many application problems. The use of expert knowledge or machine learning techniques to create rules does not exhaust the problems of representing data and decision dependencies, therefore we propose a hybrid/mixed technique for creating a set of rules while effectively modeling uncertainty through interval-valued fuzzy representation in the problem of detecting falls of elderly people. The obtained prediction confirms the correctness of the choice of diagnostic methodology.