Extreme multi-label learning method based on space-time network clustering reduction integration
A learning method and multi-label technology, applied in the field of multi-label text mining, can solve problems such as ignoring label sparsity, label-level model training, and poor learning scalability, so as to solve time and space consumption, improve representation ability, and improve general chemical effect
Pending Publication Date: 2022-06-28
YUNNAN UNIV
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The invention discloses an extreme multi-label learning method based on space-time network clustering reduction integration in the technical field of multi-label text mining. The extreme multi-label learning method comprises the following steps: space-time network attention integration characterization; self-adaptive label relation enhancement and clustering reduction learning are carried out; carrying out unbalanced learning on the weighted reduction label set; according to the method, interactive attention among the words, the phrases and the labels in the multi-label text is integrated, the dependency relationship among the words, the phrases and the labels is explored, and the extreme multi-label text characterization capability is effectively improved; a self-adaptive label relation enhancement and clustering reduction learning mechanism is provided, through self-adaptive label relation enhancement, the dependency relation between the labels can be effectively mined, the generalization of the model is improved, and through clustering reduction learning, the labels of different magnitudes can be effectively adapted to the existing model for training; a weighted reduced label set imbalance learning mechanism is provided, and the problems of poor model generalization and expandability and the like caused by label sparsity and imbalance are solved.
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