Abstract:

In ensemble pruning,a new genetic algorithm with dynamically adaptive crossover strategies is proposed for the ensemble pruning of classifiers to simultaneously and efficiently select high-quality,independent classifiers.The method dynamically updates the priority of each crossover strategy using roulette wheel blocking and greedy strategies,calculates the probability of each strategy being selected based on the priority, and thus adaptively selecting different crossover strategies during the iteration of the algorithm.The method also considers the dynamic adaptive change of crossover probability and mutation probability and integrates the selected classifiers using majority voting to obtain the final result.The proposed method in the study is compared with some ensemble pruning methods on five real face expression data sets.The experimental results show that the proposed method can select classifiers with better results and lower redundancy,and it has the lowest error of 22.50% on the CK+ data set.

Key words: face expression recognition, ensemble pruning, multi-objective genetic algorithm, roulette wheel blocking, adaptive crossover strategy, dynamic crossover probability, dynamic mutation probability, majority voting