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A Similarity Measures Algorithm for CBR Based on Matrix Iterative Learning

Lichcuan Gu, Zhiwei Ni, Zhang Jun Wu


With the rapid development of case-based reasoning (CBR) techniques such as case retrieval and case adaptation, CBR has been widely applied to various real-world applications. A successful case-based reasoning system requires a high-quality case base, which provides rich and efficient solutions for solving real-world problems. Similarity measure is not only the center part of Case-Based Reasoning systems, but also the key step of Case retrieval. In this paper, one algorithm is derived to learn the kernel matrix for capturing the relations between the case structure units based on matrix iterative analysis. For the performance evaluation, the proposed algorithm is applied to data of Pear Scab Forecasting-system. Comparing with the kernel matrix leaning algorithm based on the other methods, the experimental results show that the kernel matrix leaning algorithms based on matrix iterative analysis not only acquires higher precision but also needs less training documents and cost.


Case-based reasoning, Similarity measure, Matrix learning, Case retrieval.

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