Examining Swarm Intelligence-based Feature Selection for Multi-Label Classification


  • Awder Ahmed Sulaimani Polytechnic University
  • Adnan Mohsin Abdulazeez Duhok Polytechnic University


Feature Selection, Wrapper Methods, Filter Methods, Multi-Label Classification


Multi-label classification addresses the issues that more than one class label assigns to each instance. Many real-world multi-label classification tasks are high-dimensional due to digital technologies, leading to reduced performance of traditional multi-label classifiers. Feature selection is a common and successful approach to tackling this problem by retaining relevant features and eliminating redundant ones to reduce dimensionality. There is several feature selection that is successfully applied in multi-label learning. Most of those features are wrapper methods that employ a multi-label classifier in their processes. They run a classifier in each step, which requires a high computational cost, and thus they suffer from scalability issues. To deal with this issue, filter methods are introduced to evaluate the feature subsets using information-theoretic mechanisms instead of running classifiers. This paper aims to provide a comprehensive review of different methods of feature selection presented for the tasks of multi-label classification. To this end, in this review, we have investigated most of the well-known and state-of-the-art methods. We then provided the main characteristics of the existing multi-label feature selection techniques and compared them analytically.




How to Cite

Ahmed, A., & Abdulazeez, A. M. . (2021). Examining Swarm Intelligence-based Feature Selection for Multi-Label Classification. Journal of Soft Computing and Data Mining, 2(2), 63–74. Retrieved from https://publisher.uthm.edu.my/ojs/index.php/jscdm/article/view/7900