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- W2915012007 abstract "Data Mining is the technique of analyzing large amount of data to determine the relation among large dataset. In this paper, we are discussing a new method for document classification. Usually Document classification has been done by using classifier algorithms. Naive Bayes classifier is frequently used for classification which provides more accurate result for larger dataset. The usage of information gain with naive Bayes classifier reduces the length of branches by selecting maximum gain which produces more accurate result in classification. We propose a new methodology of assigning weights using information gain with naive Bayes classifier. The performance of naive Bayes learning with weighted gain increases accuracy than any other traditional methods using naive Bayes. The experimental result indicates that the proposed system could improve the performance of naive Bayes significantly." @default.
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- W2915012007 date "2018-12-01" @default.
- W2915012007 modified "2023-10-18" @default.
- W2915012007 title "Iterative Feature Selection Using Information Gain & Naïve Bayes for Document Classification" @default.
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- W2915012007 doi "https://doi.org/10.1109/iccitechn.2018.8631971" @default.
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