基于链分解的多标签分类属性约简
Attribute Reduction for Multi-Label Classification Based on Chain Decomposition
摘要: 本文提出了基于链分解的多标签属性约简方法。通过考虑标签之间的相关性，将标签进行排序，根据排序方法，多标签问题被分解成单标签链的形式，对于链中每一个子问题通过粗糙集方法重新定义下近似、正域、依赖度，并进行属性约简。实验结果表明，该方法能在不降低分类精度的情况下去除大部分冗余属性。
Abstract: In this paper, a new multi-label attribute reduction algorithm based on the chain decomposition is proposed. Considering the correlation between the labels, the labels are sorted. According to the sorting method, the multi-label problem is decomposed into a single-label problem chain. For each sub-problem, the lower approximation, the positive region and the dependency are redefined by the rough set method, and the attributes are reduced. Experimental results show that the algo-rithm can remove most of the redundant attributes without reducing the classification accuracy.
文章引用：张莹. 基于链分解的多标签分类属性约简[J]. 数据挖掘, 2020, 10(4): 240-246. https://doi.org/10.12677/HJDM.2020.104025

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