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References

The implementation follows the RIMER (Rule-base Inference Methodology using the Evidential Reasoning approach) framework and its adaptive-training extensions.

Core methodology

  • RIMER methodology 1 introduced the Belief Rule Base inference framework that this library implements.
  • Adaptive training 2 describes the parameter-learning approach used by BRBModel.fit().

Applications

  • Pipeline leak detection 3 is the canonical BRB application, reproduced in notebooks/03_expert_knowledge.ipynb.
  • INFRINGER 4 uses BRBs to learn decision-maker preferences in interactive multi-objective optimisation. This library originated as the machine-learning core of INFRINGER.

  1. Jian-Bo Yang, Jun Liu, Jin Wang, How-Sing Sii, and Hong-Wei Wang. Belief rule-base inference methodology using the evidential reasoning approach – rimer. IEEE Transactions on Systems, Man, and Cybernetics – Part A: Systems and Humans, 36(2):266–285, 2006. doi:10.1109/TSMCA.2005.851270. ↩

  2. Yu-Wang Chen, Jian-Bo Yang, Dong-Ling Xu, Zhi-Jie Zhou, and Da-Wei Tang. Inference analysis and adaptive training for belief rule based systems. Expert Systems with Applications, 38(10):12845–12860, 2011. doi:10.1016/j.eswa.2011.04.077. ↩

  3. Dong-Ling Xu, Jun Liu, Jian-Bo Yang, Guang-Ping Liu, Jin Wang, Ian Jenkinson, and Jun Ren. Inference and learning methodology of belief-rule-based expert system for pipeline leak detection. Expert Systems with Applications, 32(1):103–113, 2007. ↩

  4. Giovanni Misitano. Interactively learning the preferences of a decision maker in multi-objective optimization utilizing belief-rules. In 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 133–140. 2020. ↩