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Article type: Research Article
Authors: Senthilkumar, D.a; * | Reshmy, A.K.b | Paulraj, S.c
Affiliations: [a] Department of Computer Science and Engineering, University College of Engineering, Anna University, Tiruchirappalli, Tamil Nadu, India | [b] Department of Computational Intelligence, School of Computing, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur Campus, Chengalpattu, Tamil Nadu, India | [c] Department of Mathematics, College of Engineering Guindy Campus, Anna University, Chennai, Tamil Nadu, India
Correspondence: [*] Corresponding author. Dr. D. Senthilkumar, Department of Computer Science and Engineering, University College of Engineering, Anna University, Tiruchirappalli, Tamil Nadu, India. E-mails: desent07@gmail.com; senthilkumar@aubit.edu.in
Abstract: Multi-Target Regression (MTR) is used to study the relationship between the same set of input variables and multiple continuous target variables simultaneously. A dataset with many input and output variables is the prime issue to address in the MTR, which is computationally complex to build a prediction model. Also, dimensionality reduction from multiple target variables is a challenging and essential task that aims to reduce the size of the dataset to optimize the time complexity of analysis and remove the redundant and irrelevant variables. This paper proposes an efficient feature selection strategy, Multi-Target Feature Subset Selection (MTFSS), for MTR that constructs a unique subset of features by considering multiple targets. On the other hand, two feature evaluators, correlation and ReliefF, support the MTR dataset without discretization. Furthermore, two new score functions, weighted mean aggregation strategy and threshold function, are introduced to identify the significant features. To evaluate the effectiveness of the proposed MTFSS, experiments were carried out on a benchmark dataset. The experimental results demonstrate that the proposed MTFSS can select fewer features and perform better than the original dataset results. Also, the correlation-based feature evaluator performs better than ReliefF with better performance.
Keywords: Multi-target regression, feature selection, correlation, ReliefF
DOI: 10.3233/JIFS-220412
Journal: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1069-1083, 2023
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