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MBNR: Case-based reasoning with local feature weighting by neural network. (English) Zbl 1101.68781

Summary: Our aim is to build an integrated learning framework of neural network and case-based reasoning. The main idea is that feature weights for case-based reasoning can be evaluated by neural networks. In this paper, we propose MBNR (Memory-Based Neural Reasoning), case-based reasoning with local feature weighting by neural network. In our method, the neural network guides the case-based reasoning by providing case-specific weights to the learning process. We developed a learning algorithm to train the neural network to learn the case-specific local weighting patterns for case-based reasoning. We showed the performance of our learning system using four datasets.

MSC:

68T05 Learning and adaptive systems in artificial intelligence
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