A new hybrid feature selection approach using feature association map for supervised and unsupervised classification. (1st December 2017)

• Record Type: Journal Article
• Authors: Das, Amit Kumar; Goswami, Saptarsi; Chakrabarti, Amlan; Chakraborty, Basabi
• Is Part Of: Expert systems with applications. Volume 88(2017)
• Page Start: 81
• Page End: 94
• Publication Date: 2017-12-01
• Subjects: Feature selection -- Graph theory -- Classification -- Clustering; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2017.06.032
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: The algorithm visually partitions the redundant and non-redundant features. Visual partition enables right strategy adoption for right group of features. Graph-theoretic principle (vertex cover, independent set) used for subset selection. Algorithm applies for both supervised as well as unsupervised feature selection. Better results (accuracy/purity) than benchmark supervised/unsupervised algorithms. Abstract: Feature selection, both for supervised as well as for unsupervised classification is a relevant problem pursued by researchers for decades. There are multiple benchmark algorithms based on filter, wrapper and hybrid methods. These algorithms adopt different techniques which vary from traditional search-based techniques to more advanced nature inspired algorithm based techniques. In this paper, a hybrid feature selection algorithm using graph-based technique has been proposed. The proposed algorithm has used the concept of Feature Association Map (FAM) as an underlying foundation. It has used graph-theoretic principles of minimal vertex cover and maximal independent set to derive feature subset. This algorithm applies to both supervised and unsupervised classification. The performance of the proposed algorithm has been compared with several benchmark supervised and unsupervised feature selection algorithms and found to be better than them. Also, the proposed algorithm is less computationally expensive and hence has taken less execution time for the publicly available datasets used in the experiments, which include high-dimensional datasets.