A two-dimensional (2-D) learning framework for Particle Swarm based feature selection

PATTERN RECOGNITION

卷:76

Article

Hafiz, Faizal¹  Swain, Akshya¹  Patel, Nitish¹  Naik, Chirag² 

[1] Univ Auckland, Dept Elect & Comp Engn, Auckland, New Zealand

[2] Sarvajanik Coll Engn & Technol, Surat, India

关键词: Classification;    Dimensionality reduction;    Feature selection;    Particle Swarm Optimization;    Machine learning;   

DOI  :  10.1016/j.patcog.2017.11.027

来源: Elsevier

【 摘 要 】

This paper proposes a new generalized two dimensional learning approach for particle swarm based feature selection. The core idea of the proposed approach is to include the information about the subset cardinality into the learning framework by extending the dimension of the velocity. The 2D-learning framework retains all the key features of the original PSO, despite the extra learning dimension. Most of the popular variants of PSO can easily be adapted into this 2D learning framework for feature selection problems. The efficacy of the proposed learning approach has been evaluated considering several benchmark data and two induction algorithms: Naive-Bayes and k-Nearest Neighbor. The results of the comparative investigation including the time-complexity analysis with GA, ACO and five other PSO variants illustrate that the proposed 2D learning approach gives feature subset with relatively smaller cardinality and better classification performance with shorter run times. (C) 2017 Elsevier Ltd. All rights reserved.

【 授权许可】

Free