TOPSIS-ACO based feature selection for multi-label classification

Abstract

This paper introduces an approach for optimal feature selection (FS) in multi-label (ML) data, where each sample can be associated with multiple class labels. The complexity of the feature space is significantly increased in comparison to single-label data, making decision-making challenging. Therefore, FS plays a crucial role in ML classification. The proposed method, called ML-TOPSIS-ACO, leverages the ‘Technique of Order Preference by Similarity to Ideal Solution’ (TOPSIS) as a Multi-Criteria Decision Making (MCDM) technique and ‘Ant Colony Optimization’ (ACO) as a meta-heuristic technique. The ML-TOPSIS-ACO method operates in two phases. In the first phase, Modified TOPSIS (MTOPSIS) is used to perform feature ranking and select the most important features. These selected features are then passed to Modified-ACO for feature re-ranking, aiming to identify the best subset of features for ML classification. To evaluate the effectiveness of ML-TOPSIS-ACO, experiments were conducted on nine benchmark datasets. The experimental results demonstrate that ML-TOPSIS-ACO outperforms other existing methods, achieving a winning percentage of 83%.

Keywords:

• Multi-label classification
• TOPSIS
• ACO
• feature selection
• MCDM

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Gurudatta Verma

Gurudatta Verma is a Research Scholar in the Department of Information Technology, National Institute of Technology Raipur, India. Author’s area of interest are Association Rule Mining, Machine Learning and Optimization.

Tirath Prasad Sahu

Tirath Prasad Sahu is an Assistant Professor in the Department of Information Technology, National Institute of Technology Raipur, India. Author’s area of interest are Data Mining, Text Mining, Social Mining, Machine Learning, Feature Selection, Metaheuristics, Bioinformatics, Image Processing and Analysis, etc.