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- W1625746668 abstract "This paper proposes an improved SVM based multi-label classification method by using relationship among labels. Following a traditional multi-label solution, binary relevance (BR) method is first used to decompose the multi-label classification problem into multiple binary classification sub-problems, each of which is solved by an SVM classifier. By using Platt's sigmoid technique, each SVM classifier gives probability output for the following correction. A probability model is introduced to estimate the relationship among labels. The extracted label relationship is then applied to correct the outputs of SVM classifiers, in which a dynamic weight strategy is further introduced. Numerical experiments on widely used benchmark datasets show that the proposed method can improve the accuracy of multi-label classification when compared with traditional BR method and some other conventional multi-label classification methods." @default.
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- W1625746668 date "2015-07-01" @default.
- W1625746668 modified "2023-09-26" @default.
- W1625746668 title "Improving SVM based multi-label classification by using label relationship" @default.
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- W1625746668 doi "https://doi.org/10.1109/ijcnn.2015.7280497" @default.
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