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- W62465263 abstract "In this paper, we apply weighted Mutual Information for effective feature selection. The presented hybrid filter wrapper approach resembles the well known AdaBoost algorithm by focusing on those samples that are not classified or approximated correctly using the selected features. Redundancies and bias of the employed learning machine are handled implicitly by our approach.In experiments, we compare the weighted Mutual Information algorithm with other basic approaches for feature subset selection that use similar selection criteria. The efficiency and effectiveness of our method are demonstrated by the obtained results." @default.
- W62465263 created "2016-06-24" @default.
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- W62465263 date "2011-01-01" @default.
- W62465263 modified "2023-10-03" @default.
- W62465263 title "Weighted Mutual Information for Feature Selection" @default.
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- W62465263 doi "https://doi.org/10.1007/978-3-642-21738-8_24" @default.
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