Feature Selection and Overlapping Clustering-Based Multilabel Classification Model

Abstract EN

Multilabel classification (MLC) learning, which is widely applied in real-world applications, is a very important problem in machine learning.

Some studies show that a clustering-based MLC framework performs effectively compared to a nonclustering framework.

In this paper, we explore the clustering-based MLC problem.

Multilabel feature selection also plays an important role in classification learning because many redundant and irrelevant features can degrade performance and a good feature selection algorithm can reduce computational complexity and improve classification accuracy.

In this study, we consider feature dependence and feature interaction simultaneously, and we propose a multilabel feature selection algorithm as a preprocessing stage before MLC.

Typically, existing cluster-based MLC frameworks employ a hard cluster method.

In practice, the instances of multilabel datasets are distinguished in a single cluster by such frameworks; however, the overlapping nature of multilabel instances is such that, in real-life applications, instances may not belong to only a single class.

Therefore, we propose a MLC model that combines feature selection with an overlapping clustering algorithm.

Experimental results demonstrate that various clustering algorithms show different performance for MLC, and the proposed overlapping clustering-based MLC model may be more suitable.