关键词: 特征选择, 双模糊, 最小学习机, 模糊隶属度, 模糊补充

Abstract: Based on the traditional feature selection algorithms, this paper proposes a new feature selection algorithm based on doubly fuzziness information (FSA-DFI). One fuzziness is involved in the fuzzified version of least learning machine (LLM), i.e., FUZZY-LLM, and the other fuzziness is involved in the contribution-rate based fuzzy complementary method in which the features with high contribution rates may be finally selected. The proposed feature selection algorithm is built on the combination of FUZZY-LLM and the contribution-rate based fuzzy complementary method. Experiments indicate that the proposed feature selection algorithm has better classification accuracy, better dimension reduction and faster learning speed, in contrast to other comparative algorithms.

Key words: feature selection, doubly fuzziness, least learning machine, fuzzy membership degree, fuzzy complementary