GA-Optimized Feature Weighting for Fuzzy C-Means Classification
The problem of insufficient classification accuracy in fuzzy clustering algorithms for multidimensional data is addressed in this paper. To tackle this issue, an improved genetic algorithm (GA)-based fuzzy controller is proposed, which combines the advantages of an improved genetic algorithm and a fuzzy C-means (FCM) clustering algorithm. The population initialization is performed using the Tent chaotic map, while the best individual retention strategy and last elimination selection operator are employed to adjust the population structure. Furthermore, an elitist crossover operator, an adaptive trial mutation operator, and a nonlinear convergence factor are introduced to mitigate the risk of falling into local optima. The controller algorithm integrates the intermediate parameters of FCM clustering into the fitness function of the genetic algorithm, and effectively improves the classification accuracy by screening the optimal feature subset and then fuzzy clustering. The experimental results on the Pistachio, WDBC, and Wine datasets show that the proposed method achieves competitive classification accuracy compared with other FCM-based feature-weighting optimization methods.