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Case-Based-Reasoning Decision Method with Generalized Combination Rule
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AI
DOI：10.3390/systems14050587.png)
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Abstract
En 中文
Case-based reasoning (CBR) is an efficient intelligent decision-making approach, but traditional methods often neglect the weight and reliability of decision information and struggle with attribute heterogeneity and missing data. This study proposes a novel CBR method based on the generalized combination (GC) rule to overcome these limitations. We design differentiated similarity calculations for heterogeneous attributes, and construct basic probability assignments (BPAs) by grouping historical cases with identical similarity to handle missing data. Then, Deng entropy and Jousselme distance are used to characterize attribute weight and reliability, respectively. Discounted BPAs are recursively fused via the GC rule, and final decisions are derived through Bayesian approximation. A case study of typhoon disaster emergency decision-making demonstrates the superior performance of the proposed method.
Keywords:
case-based reasoning
generalized combination rule
evidence fusion
basic probability assignment
emergency decision-making
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