As feature weight allocation and case base maintenance have an important influence on the performance of Case-Based Reasoning(CBR) classifier, a CBR algorithm model named Ant lion and Expectation maximization of Gaussian mixture model CBR(AGECBR) was proposed, in which the Ant Lion Optimizer(ALO) was used to allocate weights and Expectation Maximization algorithm of Gaussian Mixture Model(GMMEM) was used for case base maintenance. Firstly, the ALO was used to allocate the feature weights. In this process, the classification accuracy of CBR was used as the fitness function of the ALO to iteratively optimize the feature weights, so as to achive the optimized allocation of feature weights. Secondly, the expectation maximization algorithm of Gaussian mixture model was used to perform clustering analysis to each case in the case base, and the noise cases and redundant cases in the base were deleted, so as to realize the maintenance of the case base. The experiments were carried out on the UCI standard datasets, in which, AGECBR has the average classification accuracy 3.83-5.44 percentage points higher than Back Propagation(BP), k-Nearest Neighbor(kNN) and other classification algorithms. Experimental results show that the proposed method can effectively improve the accuracy of CBR classification.

Aiming at the optimal allocation problem of attribute weights in Case-Based Reasoning (CBR) classifier, an introspective learning-based iterative adjustment approach for the attribute weights was proposed. The attribute weights could be adjusted according to the classification result of the training case by CBR classifier. Based on the success-driven weight learning strategy, if the current training case was classified successfully, the weights of matched attributes would be increased and the weights of mismatched attributes would be decreased according to weight adjustment formulas, then all of the weights would be normalized as the new weights of the current iteration. The experimental results show that the accuracy on UCI dataset PD, Heart and WDBC of CBR classifier with the proposed method are respectively 1.72%, 4.44% and 1.05% higher than the traditional CBR classifier. This illustrates that success-driven introspective learning method for the weights adjustment can improve the rationality of weight allocation, and then improve the accuracy of CBR classifier.