Case reasoning classifier case retrieval method

Abstract

The invention discloses a case reasoning classifier case retrieval method. The method comprises the steps of S1, case library protocol feature selection; S2, clustering decision tree training on a C4.5 algorithm and a case library; and S3, a weighted voting KNN method. According to the method, improved research is carried out on a case retrieval method of a case reasoning classifier: a method of integrating and using a inductive index strategy in neighbor case retrieval is provided, and a feature evaluation method of an information gain ratio is adopted to select a feature training case library clustering decision tree with a relatively remarkable information gain ratio; during case retrieval, a cluster corresponding to a target case is retrieved according to the clustering decision tree, and then neighbor case retrieval is performed in the case cluster. Experimental data of the open source data set show that compared with a traditional CBR model, the improved model can fully utilize the classification capability of case characteristics to carry out case retrieval, the calculation time complexity of the CBR reasoning classifier is effectively reduced, and the reasoning accuracy is improved.

Description

technical field [0001] The invention specifically relates to a case reasoning method, in particular to an improved case retrieval method for a case reasoning classifier. Background technique [0002] Case-based reasoning (CBR) is an analogy-solving method in the field of artificial intelligence. It originated from the exploration and research of human reasoning and learning mechanisms from the perspective of cognitive science. Cognitive science and artificial intelligence expert Roger Sehank proposed on the basis of dynamic memory theory, and Professor Janel L. Kolodner of Georgia Institute of Technology first developed the application of CBR expert system. When using analogy to solve a new problem (or target case), it is necessary to recall the past experience cases (source cases) of similar problems for reasoning. Therefore, in addition to storing empirical case data and case solutions, the computational model of the case reasoning system should also have the skills to ob...

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Problems solved by technology

Case feature reduction reduces the data storage requirements for case neighbor retrieval and improves the efficiency of CBR case retrieval, but it violates the original intention of expanding case features in CBR applications and is not conducive to the retrieval of sparse data. The corresponding research results did not examine large-scale data Classification performance on the set

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