¹Scientific Research Office, Chongqing University of Science and Technology, Chongqing, 40133,China
²School of Electric and Information Engineering, Chongqing University of Science and Technology, Chongqing, 40133,China

---

Abstract—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.

Index Terms—relief algorithm, feature selection, data mining, speech recognition

[PDF]

Cite: Lun Gao, Taifu Li, Lizhong Yao, Feng Wen, "Research and Application of Data Mining Feature Selection Based on Relief Algorithm," Journal of Software vol. 9, no. 2, pp. 515-522, 2014.