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- W4323894633 abstract "The thesis data is increasing yearly; classification is one method that can be used to obtain added information from this case. The classification that suits this case is the multi-label classification. The multi-label classification with the Problem Transformation approach is a flexible approach. In this study, a comparison of the Problem Transformation methods, namely Binary Relevance, Label Powerset, and Classifier Chain, was carried out with the Multinomial Naïve Bayes algorithm as an estimator. As a result, the Classifier Chain method gives results that tend to be better, with the value of Hamming Loss being 0.0321 and an accuracy of 64.60%." @default.
- W4323894633 created "2023-03-12" @default.
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- W4323894633 creator A5048510495 @default.
- W4323894633 date "2022-12-13" @default.
- W4323894633 modified "2023-09-27" @default.
- W4323894633 title "The Best Problem Transformation Method in Multi-Label Classification Text for Thesis Abstract" @default.
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- W4323894633 doi "https://doi.org/10.1109/icitisee57756.2022.10057824" @default.
- W4323894633 hasPublicationYear "2022" @default.
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