OCBMLC: An overlapping clustering based multi-label classification algorithm
Liwen Peng, Yongguo Liu, Huan Liao, Peng Zhang · 2017
This paper explores the clustering based multi-label classification problem. We propose an overlapping clustering based multi-label classification method (OCBMLC). In the experiment we compare three multi-label classification algorithms that are based on clustering. In the experimental setting section, we use three clustering methods for datasets before multi-label classification. Two popular multi-label benchmark datasets are used in our experiments: emotions dataset and yeast dataset. We employ micro F1-measure and hamming loss as multi-label classification performance evaluation metrics. The experimental results demonstrate that various clustering algorithms generate different performance of the multi-label classification, and the overlapping clustering based multi-label classification algorithm may adapt to multi-label classification more.