一种基于关联规则的MLKNN多标签分类算法

针对MLKNN算法仅对独立标签进行处理，忽略现实世界中标签之间相关性这一问题，提出了一种基于关联规则的MLKNN多标签分类算法(FP-MLKNN)。该算法采用关联规则算法挖掘标签之间的高阶相关性，并用标签之间的关联规则改进MLKNN算法,以达到提升分类性能的目的。首先，使用MLKNN算法求样本的特征置信度；采用关联规则算法挖掘生成一系列强关联规则，进而将2种算法进行融合来构造多标签分类器，对新标签进行预测；在此基础上，将本文提出的算法与MLKNN、AdaBoostMH和BPMLL这3种算法进行实验对比。实验结果表明，本文所提算法在yeast、emotions和enron数据集上的分类性能均优于这3种算法，具有较好的分类效果。

A MLKNN multi-label classification algorithm based on association rules

Aiming at the problem that the MLKNN algorithm ignores the correlation between labels in the real world when dealing with independent labels, this paper proposes an MLKNN multi-label classification algorithm (FP-MLKNN) based on association rules. The algorithm uses association rules to mine high-order correlations between labels, and applies the association rules between labels to the MLKNN algorithm for improvement to achieve the purpose of improving the classification performance. Firstly, the MLKNN algorithm is used to obtain the characteristic confidence of the sample. Secondly, the association rule algorithm is used to mine and generate a series of strong association rules. Thirdly, the two algorithms are fused to construct a multi-label classifier to predict new labels. Experimental results show that the proposed algorithm has better classification performance than MLKNN, AdaBoostMH and BPMLL algorithms on yeast, emotions, and enron datasets, achieving a good classification effect.