Feature Weights Optimization Based on SHO-SA Algorithm for Case-based Reasoning Prediction Model

Performance of the case-based reasoning (CBR) prediction model is directly affected by feature weight allocation in the retrieval process. A method based on selfish herd optimizer-simulated annealing (SHO-SA) algorithm was proposed to calculate the feature weights. The root mean square error (RMSE) of the CBR prediction model was first defined as the fitness function in the SHO algorithm and the SA algorithm to evaluate the rationality of the weight distribution. Then, the weights distribution with the minimum RMSE in the population were obtained through the herd movement, predation and recovery steps of the SHO. Finally, SA algorithm was employed to search randomly based on the above weight, and an approximate optimal solution of the feature weights was obtained. Performance evaluation was carried out by five benchmark regression datasets from University of California Irvine (UCI) datasets. Results show that compared with other typical optimization methods, the proposed method can improve the accuracy of the CBR prediction model significantly. Meanwhile, it illustrates that the matter of SHO suffering from local minima can be mended by SA algorithm.