Feature selection based on feature similarity measure
Lianxi Wang · Computer Engineering and Applications Journal · 2010
This paper proposes a feature selection algorithm based on feature similarity measure.The method clusters features based on similarity measure and then chooses representative features from each cluster.At last,the feature subset is selected by removing the feature which is less relevant or irrelevant to class feature.Theory analysis indicates that the method with lower time complexity can be applied in feature selection for high dimensional data.The superiority of the algorithm,in terms of dimensionality reduction and classification performance,is established extensively over UCI datasets through comparing with other classic feature selection approaches.