A feature selection approach based on NSGA-II with ReliefF

Abstract

In this paper, a hybrid feature selection algorithm based on a multi-objective algorithm with ReliefF (MOFS-RFGA) is proposed. Combining the advantages of filter and wrapper methods, the two types of algorithms are hybridized to improve the capability when solving feature selection problems. First, the ReliefF algorithm is used to score the features according to their importance to the instance class. Then, the feature scoring information is used to initialize the population. Also, a new crossover and mutation operator are designed in this paper to guide the crossover and mutation process based on feature scoring information to improve the search direction of MOFS-RFGA in search space and enhance the convergence performance. In the experiments, MOFS-RFGA is compared with seven advanced multi-objective feature selection algorithms on 20 datasets, and the results show that MOFS-RFGA can fully utilize the advantages of filter and wrapper methods, beating the excellent performance of the comparison algorithms on a large number of datasets, and ensuring good classification performance while cutting a large number of features.