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- W2904649346 abstract "Abstract Feature selection technique has been a very active research topic that addresses the problem of reducing the dimensionality. Whereas, datasets are continuously growing over time both in samples and features number. As a result, handling both irrelevant and redundant features has become a real challenge. In this paper we propose a new straightforward framework which combines the horizontal and vertical distributed feature selection technique, called Horizo-Vertical Distributed Feature Selection approach (HVDFS), aimed at achieving good performances as well as reducing the number of features. The effectiveness of our approach is demonstrated on three well-known datasets compared to the centralized and the previous distributed approach, using four well-known classifiers." @default.
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- W2904649346 date "2018-11-01" @default.
- W2904649346 modified "2023-09-24" @default.
- W2904649346 title "A New Horizo-Vertical Distributed Feature Selection Approach" @default.
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- W2904649346 doi "https://doi.org/10.2478/cait-2018-0045" @default.
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