A graph-based approach for feature selection from higher order correlations. (2018)

• Record Type: Journal Article
• Authors: Das, Sunanda; Das, Asit Kumar
• Is Part Of: International journal of computational systems engineering. Volume 4:Number 1(2018)
• Page Start: 66
• Page End: 71
• Publication Date: 2018
• Subjects: graph technology -- score -- correlation -- feature selection; Computer science -- Periodicals; Electronic data processing -- Periodicals; System analysis -- Periodicals; 003.3
• Languages: English
• ISSNs: 2046-3391

Abstract

Graph technology emerges as an important topic in the field of data mining and machine learning community. The analysis of high-dimensional data is crucial to identify a smaller subset of features which are informative for classification and clustering. In this paper, an efficient graph feature selection method is proposed to render the analysis of high-dimensional data tractable. Here, the feature scores are calculated for obtaining the weights of the edges in the weighted graph to identify the optimal feature subset. One advantage of this method is that it can successfully identify the optimal features for machine learning. The experimental results on our dataset verify the effectiveness and efficiency of the proposed method.