A Supervised Feature Selection Method For Mixed-Type Data using Density-based Feature Clustering

• URL: http://arxiv.org/abs/2111.08169v1
• Date: Wed, 10 Nov 2021 15:05:15 GMT
• Title: A Supervised Feature Selection Method For Mixed-Type Data using Density-based Feature Clustering
• Authors: Xuyang Yan, Mrinmoy Sarkar, Biniam Gebru, Shabnam Nazmi, and Abdollah Homaifar
• Abstract summary: This paper proposes a supervised feature selection method using density-based feature clustering (SFSDFC) SFSDFC decomposes the feature space into a set of disjoint feature clusters using a novel density-based clustering method. Then, an effective feature selection strategy is employed to obtain a subset of important features with minimal redundancy from those feature clusters.
• Score: 1.3048920509133808
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Feature selection methods are widely used to address the high computational overheads and curse of dimensionality in classifying high-dimensional data. Most conventional feature selection methods focus on handling homogeneous features, while real-world datasets usually have a mixture of continuous and discrete features. Some recent mixed-type feature selection studies only select features with high relevance to class labels and ignore the redundancy among features. The determination of an appropriate feature subset is also a challenge. In this paper, a supervised feature selection method using density-based feature clustering (SFSDFC) is proposed to obtain an appropriate final feature subset for mixed-type data. SFSDFC decomposes the feature space into a set of disjoint feature clusters using a novel density-based clustering method. Then, an effective feature selection strategy is employed to obtain a subset of important features with minimal redundancy from those feature clusters. Extensive experiments as well as comparison studies with five state-of-the-art methods are conducted on SFSDFC using thirteen real-world benchmark datasets and results justify the efficacy of the SFSDFC method.

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