一种加权的ML—kNN算法

A Novel Weighted Multi-label kNN Algorithm

ML—kNN算法利用贝叶斯概率修改传统的kNN算法以解决多标签问题，但这种基于概率统计的方法对覆盖率低的标签容易造成误判。因此，该文提出了一种加权ML—kNN算法，将样本与邻居之间的距离转化为权值来改这种误判。在三个基准数据集上进行对比实验，利用七个标准对其进行评测。实验结果表明，该加权ML—kNN算法整体上优于ML—kNN算法。

ML-kNN modifies kNN by combining Bayesian probability to solve multi-label problem. However, based on probability statistics, ML-kNN doesn＂t tend to assign those labels with low occurrence frequency for samples. Thus we proposed a novel weighted ML-kNN algorithm by concerning distances between a sample and its neighbors. We evaluated its performance on three benchmark datasets with seven metrics. The experiment results show that the weighted ML-kNN algorithm has better performance than ML-kNN on the whole.