SemiACO: A semi-supervised feature selection based on ant colony optimization. (15th March 2023)

• Record Type: Journal Article
• Authors: Karimi, Fereshteh; Dowlatshahi, Mohammad Bagher; Hashemi, Amin
• Is Part Of: Expert systems with applications. Volume 214(2023)
• Publication Date: 2023-03-15
• Subjects: Semi-supervised Feature Selection -- Ant Colony Optimization -- Reinforcement Learning -- Nonlinear Heuristic Function -- Temporal difference; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.119130
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: We proposed an ACO-based algorithm for semi-supervised feature selection. A non-linear function is used to search the feature space. A heuristic learning is utilized using temporal difference reinforcement learning. A combination of relevancy and redundancy methods is used. The proposed method outperforms competitive methods. Abstract: Feature selection is one of the most efficient procedures for reducing the dimensionality of high-dimensional data by choosing a practical subset of features. Since labeled samples are not always available and labeling data may be time-consuming or costly, the importance of semi-supervised learning becomes apparent. Semi-supervised learning deals with data that includes both labeled and unlabelled instances. This article proposes a method based on Ant Colony Optimization (ACO) for the semi-supervised feature selection problem called SemiACO. The SemiACO algorithm finds features by considering the minimum redundancy between features and the maximum relevancy between the features and the class label. The SemiACO uses a nonlinear heuristic function instead of a linear one. The heuristic learning technique for the ACO heuristic function utilize a Temporal Difference (TD) reinforcement learning algorithm. We characterize the feature selection search space as a Markov Decision Process (MDP), where features indicate the states, and selecting the unvisited features by each ant represents a set of actions. We contrast the efficiency of SemiACO based on various experiments on 14 benchmark datasets, comparing eight semi-supervised feature selection methods.