ANovel Feature Selection Measure Partnership-Gain

International journal of online engineering

Mostafa A. Salama¹ 

[1] British University in Egypt - BUE

关键词: Feature Selection;    Interdependency between features;    Classification;   

DOI  :  

学科分类：社会科学、人文和艺术（综合）

来源: International Association of Online Engineering

【 摘 要 】

Multivariate feature selection techniques search for the optimal features subset to reduce the dimensionality and hence the complexity of a classification task. Statistical feature selection techniques measure the mutual correlation between features well as the correlation of each feature to the tar- get feature. However, adding a feature to a feature subset could deteriorate the classification accuracy even though this feature positively correlates to the target class. Although most of existing feature ranking/selection techniques consider the interdependency between features, the nature of interaction be- tween features in relationship to the classification problem is still not well investigated. This study proposes a technique for forward feature selection that calculates the novel measure Partnership-Gain to select a subset of features whose partnership constructively correlates to the target feature classification. Comparative analysis to other well-known techniques shows that the proposed technique has either an enhanced or a comparable classification accuracy on the datasets studied. We present a visualization of the degree and direction of the proposed measure of features’ partnerships for a better understanding of the measure’s nature.

【 授权许可】

Unknown