Authors: Matijević, Luka 
Affiliations: Computer Science 
Title: Variable Neighborhood Search for Multi-label Feature Selection
Series/Report no.: Lecture Notes in Computer Science
Volume: 13367
First page: 94
Last page: 107
Conference: International Conference on Mathematical Optimization Theory and Operations Research MOTOR 2022: Mathematical Optimization Theory and Operations Research
Issue Date: 2022
Rank: M33
DOI: 10.1007/978-3-031-09607-5_7
With the growing dimensionality of the data in many real-world applications, feature selection is becoming an increasingly important preprocessing step in multi-label classification. Finding a smaller subset of the most relevant features can significantly reduce resource consumption of model training, and in some cases, it can even result in a model with higher accuracy. Traditionally, feature selection has been done by employing some statistical measure to determine the most influential features, but in recent years, more and more metaheuristics have been proposed to tackle this problem more effectively. In this paper, we propose using the Basic Variable Neighborhood Search (BVNS) algorithm to search for the optimal subset of features, combined with a local search method based on mutual information. The algorithm can be considered a hybrid between the wrapper and filter methods, as it uses statistical knowledge about features to reduce the number of examined solutions during the local search. We compared our approach against Ant Colony Optimization (ACO) and Memetic Algorithm (MA), using the K-nearest neighbors classifier to evaluate solutions. The experiments conducted using three different metrics on a total of four benchmark datasets suggest that our approach outperforms ACO and MA.
Keywords: K-nearest neighbors | Metaheuristics | Optimization | Mutual information
Publisher: Springer Link
Project: Advanced artificial intelligence techniques for analysis and design of system components based on trustworthy BlockChain technology - AI4TrustBC 

Show full item record


checked on May 18, 2024

Page view(s)

checked on May 9, 2024

Google ScholarTM




Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.