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• W2086093553 abstract "This paper proposes a two-level feature selection to improves Naive Bayes with kernel density estimation. The performance of the proposed feature selection is evaluated on question item set based on Bloom's cognitive levels. This two-level feature selection contains of filter and wrapper based feature selection. This paper uses chi square and information gain as the filter based feature selection and forward feature selection and backward feature elimination as the wrapper based feature selection. The result shows that the two-level feature selection improves the Naive Bayes with kernel density estimation. The combination of chi square and backward feature elimination give more optimal quality than the other combination." @default.
• W2086093553 created "2016-06-24" @default.
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• W2086093553 date "2013-10-01" @default.
• W2086093553 modified "2023-10-09" @default.
• W2086093553 title "Two-level feature selection for naive bayes with kernel density estimation in question classification based on Bloom's cognitive levels" @default.
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• W2086093553 doi "https://doi.org/10.1109/iciteed.2013.6676245" @default.
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