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• W3212062851 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." @default.
• W3212062851 created "2021-11-22" @default.
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• W3212062851 date "2021-10-17" @default.
• W3212062851 modified "2023-10-16" @default.
• W3212062851 title "A Supervised Feature Selection Method For Mixed-Type Data using Density-based Feature Clustering" @default.
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• W3212062851 doi "https://doi.org/10.1109/smc52423.2021.9659208" @default.
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