Weight optimization for case-based reasoning using membrane computing

In case-based reasoning (CBR), the weights of feature attributes directly affect the quality of problem solving. This paper proposes a membrane computing (MC)-based approach to optimize the attribute weights. A cell-like membrane structure with three layers is designed. The initial weight objects are then obtained by a global search using selection, crossover, mutation, and a two-way communication rule. Subsequently, the best weight object is obtained using a simulated annealing (SA) algorithm between the membranes. In this manner, a group of weight objects is received for CBR problem solving. The experiment results show that the classification accuracy of this method is higher compared with the entropy method, genetic algorithms, SA, and the neural networks method. The application of MC can obtain more reasonable attribute weights, which can effectively improve the quality of problem solving for a CBR system.