Research and Application of Data Mining Feature Selection Based on Relief Algorithm

To choose the best features in data mining issues, the Relief Feature Selection Algorithm is proposed to implement the feature selection in this paper. Firstly, the data of Ionosphere from the UCI (University of California Irvine) database is used to do a simulation experiment; secondly, the proposed method is employed to do feature selection for voice signal. In this case study, the study starts from the 24-dimensional parameters of MFCC (Mel Frequency Cepstrum Coefficient), the most important parameters of MFCC can be found in the voice signal; then, the 24-dimensional parameters of MFCC can be combined and optimized in the case of recognition rate not much changed. The experimental results show that the method extracts out the best features. Therefore, the research provides a new direction to feature extraction for speech recognition process.