Feature selection of medical data sets based on RS-RELIEFF
作者： Xiao Liu , Xiaoli Wang , Qiang Su
DOI: 10.1109/ICSSSM.2015.7170275
关键词: Data mining 、 Computer science 、 Noise (video) 、 MATLAB 、 Statistical classification 、 Reduction (complexity) 、 Feature (computer vision) 、 Feature selection 、 Algorithm design 、 Artificial intelligence 、 Data set 、 Pattern recognition
摘要: For most of data sets, there exist some redundant, irrelevant and even noise features. Usually, are plenty features in medical sets the correlation among is strong. So, feature selection gets great concern recent years. RELIEFF one effective algorithms, but cannot remove redundant RS a mathematical approach to intelligent analysis can novel RS- algorithm proposed this paper. In RS-RELIEFF, reduction applied set with firstly, then later, new integrative weight each condition will be got end. The was tested sets. experimental results show that RS-RELIEFF has better classification accuracy 71.2644% fewer selected
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