Work

A group decision making-based parallel multi-label feature selection for big data applications

Year: 2026

Type: article

Abstract: Extracting knowledge from data is challenging in real-world scenarios. Particularly in big data learning, the volume of the data is a significant challenge. Therefore, scalable feature selection algorithms are essential for datasets of this scale. In this paper, a parallelized multi-label feature selection algorithm is proposed using intuitionistic fuzzy sets (IFSs) and group decision making. This partitions data instances into multiple groups. Then the mutual information between each feature and label within each group is computed. Subsequently, the mutual information values are transformed into IFSs and aggregated using the intuitionistic fuzzy weighted averaging (IFWA) operator. Parallel calculations were employed for this, which makes it suitable for large number of instances. Finally, the rank of the features is obtained using the intuitionistic fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. This opens a new perspective on the feature selection by adapting group decision making for parallel execution. The experiments conducted on 15 datasets show the effectiveness of the method. Graphical abstract

Source: Journal of King Saud University - Computer and Information Sciences

DOI: 10.1007/s44443-026-00509-9

Authors: S. S. Mohanrasu, R. Rakkiyappan, Munkhjargal Gochoo

Institutions: United Arab Emirates University, Bharathiar University

Language: English

Topic: Text and Document Classification Technologies

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,990

Funder: United Arab Emirates University