Extreme multi-label learning method based on space-time network clustering reduction integration

Abstract

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.

Description

technical field [0001] The invention relates to the technical field of multi-label text mining, in particular to an extreme multi-label learning method based on space-time network clustering reduction integration. Background technique [0002] With the continuous development of Internet technology, more and more labels present a multi-label situation. Traditional multi-label learning only focuses on a relatively small amount of labels. With the increase of Internet data, the amount of labels has exceeded 10,000 or millions. , the most widely used is extreme multi-label text classification, such as short text classification such as Taobao, web page links, etc., with more than 10,000 tags; Wikipedia text classification, with more than one million tags. When faced with such a large amount of labels, the traditional multi-label learning method will not be applicable. Therefore, in view of the application limitations of traditional multi-label learning, it is of great practical s...

AI Technical Summary

Problems solved by technology

[0004] (1) The traditional multi-label model cannot adapt to the shortcomings of extreme multi-label scenarios

[0005] Traditional multi-label learning only focuses on a relatively small number of labels, such as less than 100, but with the increasing number of Internet data, the number of labels has exceeded ten thousand or one million. Due to the huge number of labels, the traditional multi-label learning method takes a long time The complexity is too large to adapt to extreme multi-label learning scenarios

[0006] (2) Low generalization performance of existing extreme multi-label learning models

[0007] Existing extreme multi-label learning methods are mainly based on tree ensembles, embeddings, and deep network end-to-end methods. These methods simply use linear or nonlinear mapping for multi-label learning, ignoring the dependencies between labels, especially when labels When the amount is huge, the labels are usually presented in a graph, which makes the generalization performance of the existing multi-label learning model low

[0008] (3) Poor scalability of existing extreme multi-label learning

[0009] Existing extreme multi-label learning methods are mainly developed for specific label magnitudes, and cannot adapt to different label magnitude models for training, and ignore the problems of label sparseness and imbalance, resulting in poor generalization and scalability of the model

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