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• W2809190716 startingPage "2027" @default.
• W2809190716 abstract "Feature selection is a dimensionality reduction technique that helps to improve data visualization, simplify learning, and enhance the efficiency of learning algorithms. The existing redundancy-based approach, which relies on relevance and redundancy criteria, does not account for feature complementarity. Complementarity implies information synergy, in which additional class information becomes available due to feature interaction. We propose a novel filter-based approach to feature selection that explicitly characterizes and uses feature complementarity in the search process. Using theories from multi-objective optimization, the proposed heuristic penalizes redundancy and rewards complementarity, thus improving over the redundancy-based approach that penalizes all feature dependencies. Our proposed heuristic uses an adaptive cost function that uses redundancy–complementarity ratio to automatically update the trade-off rule between relevance, redundancy, and complementarity. We show that this adaptive approach outperforms many existing feature selection methods using benchmark datasets." @default.
• W2809190716 created "2018-06-29" @default.
• W2809190716 creator A5005768162 @default.
• W2809190716 creator A5088692281 @default.
• W2809190716 date "2018-06-19" @default.
• W2809190716 modified "2023-10-03" @default.
• W2809190716 title "An adaptive heuristic for feature selection based on complementarity" @default.
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• W2809190716 doi "https://doi.org/10.1007/s10994-018-5728-y" @default.
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