Computer Methodology - CBR

    Case Based Reasoning (CBR) is a general AI technique used for problem-solution, fault detection and diagnosis, reasoning, learning and decision support. CBR finds solutions to new problems by adapting previously good solutions to similar problems. Case studies features need to be specified to be helpful in retrieving other cases. At the same time, features have to be discriminative enough to avoid the retrieval of cases studies which could lead to wrong solutions because of being too different. CBR does not require an explicit domain model, but just to identify new cases with significant features, which is in fact the way CBR “learns.” (De and Chakraborty, 2021).


(Barman, Biswas, Sarkar, Soni, and Purkayastha, 2020)


    CBR procedures are usually explained as the so called “CBR working cycle”, which includes five steps: (1) current problem description; (2) search for a successful solution of a similar case; (3) adaptation and reuse of the solution to the new problem; (4) evaluation; and (5) confirmed solution storage. The main CBR limitations are related to the need to get huge case studies databases, which could include non-relevant information and make the retrieval often excessively time-consuming (De and Chakraborty, 2021).





References:

  • De, S. and Chakraborty, B., 2021. Case Based Reasoning (CBR) Methodology for Car Fault Diagnosis System (CFDS) Using Decision Tree and Jaccard Similarity Method. [online] Ieeexplore.ieee.org. Available at: <https://ieeexplore.ieee.org/document/8529695> [Accessed 1 December 2021].
  • Barman R., Biswas S.K., Sarkar S., Purkayastha B., Soni B. (2020) Image Processing Using Case-Based Reasoning: A Survey. In: Mallick P.K., Meher P., Majumder A., Das S.K. (eds) Electronic Systems and Intelligent Computing. Lecture Notes in Electrical Engineering, vol 686. Springer, Singapore. https://doi.org/10.1007/978-981-15-7031-5_62

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