A novel hybrid feature selection algorithm: using ReliefF estimation for GA-Wrapper search

Abstract

A new feature selection method named ReliefF-GA-Wrapper is proposed to combine the advantages of filter and wrapper. In the ReliefF-GA-Wrapper method, the original features are evaluated by the ReliefF method, and the resulting estimation is embedded into the genetic algorithm applied to search optimal feature subset with the train accuracy of induction learning algorithm for the evaluation function. Experiments are carried on handwritten Chinese characters dataset, which is a large-scale dataset, and several other typical datasets with features more than 20. The results show ReliefF-GA-Wrapper has better performance then ReliefF and GA-Wrapper, indicating that the proposed ReliefF-GA-Wrapper algorithm is competitive and scales well to large datasets.