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• W2806931302 abstract "This paper presents a novel approach based on hybrid feature selection that significantly reduces dimensionality of features. In this paper, an efficient method consisting of ReliefF and PCA is proposed which shows remarkable results with different chronic disease datasets. The presented work is suitable for both text and micro-array datasets which determines the optimal value of threshold for the selection of relevant and non-redundant features. To validate the performance of proposed work, ten popular benchmark datasets are used. With the results obtained, it is found that the presented approach reduces more than 50% irrelevant and redundant features from the dataset. Also with the proposed method, the computation time significantly decreases for all considered chronic disease datasets. Moreover, it is experimentally depicted that the threshold value significantly affects the selection of appropriate features." @default.
• W2806931302 created "2018-06-13" @default.
• W2806931302 creator A5032996381 @default.
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• W2806931302 date "2018-01-01" @default.
• W2806931302 modified "2023-09-27" @default.
• W2806931302 title "An Efficient Hybrid Feature Selection model for Dimensionality Reduction" @default.
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• W2806931302 doi "https://doi.org/10.1016/j.procs.2018.05.188" @default.
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