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A new classification model for a class imbalanced data set using genetic programming and support vector machines: case study for wilt disease classification

Class imbalanced data set is a state where each class of the given data set is not evenly distributed. When such case happens, most standard classifiers fail to recognize examples that belong to a minority class. Hence, several methods have been proposed to solve this problem such as resampling, mod...

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書誌詳細
主要な著者:	Mohd Pozi, Muhammad Syafiq, Sulaiman, Md Nasir, Mustapha, Norwati, Perumal, Thinagaran
フォーマット:	論文
言語:	English
出版事項:	Taylor & Francis 2015
オンライン･アクセス:	http://psasir.upm.edu.my/id/eprint/43520/1/A%20new%20classification%20model%20for%20a%20class%20imbalanced%20data%20set%20using%20genetic%20programming.pdf http://psasir.upm.edu.my/id/eprint/43520/
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