Graph-Based Supervised Feature Selection Using Correlation Exponential

Kumar, Gulshan; Jain, Gitesh; Panday, Mrityunjoy; Das, Amit Kumar; Goswami, Saptarsi

doi:10.1007/978-981-13-7403-6_4

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…To determine the same, different graph approaches have been proposed for feature selection [25][26][27][28][29][30][31][32][33][34][35][36][37]. Das et al [30] used Feature Association Map to present a graph-based hybrid feature selection method.…”

Section: Introductionmentioning

confidence: 99%

“…Das et al [30] used Feature Association Map to present a graph-based hybrid feature selection method. While Kumar et al [34] employed correlation exponential in the graph-based feature selection method. Lim et al [18] presented a feature-dependency-based unsupervised feature selection (DUFS) method that uses pairwise dependency of the features to perform feature selection.…”

Section: Introductionmentioning

confidence: 99%

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Augmentation of Densest Subgraph Finding Unsupervised Feature Selection Using Shared Nearest Neighbor Clustering

et al. 2023

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Determining the optimal feature set is a challenging problem, especially in an unsupervised domain. To mitigate the same, this paper presents a new unsupervised feature selection method, termed as densest feature graph augmentation with disjoint feature clusters. The proposed method works in two phases. The first phase focuses on finding the maximally non-redundant feature subset and disjoint features are added to the feature set in the second phase. To experimentally validate, the efficiency of the proposed method has been compared against five existing unsupervised feature selection methods on five UCI datasets in terms of three performance criteria, namely clustering accuracy, normalized mutual information, and classification accuracy. The experimental analyses have shown that the proposed method outperforms the considered methods.

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