Solving feature subset selection problem by a Parallel Scatter Search |
| |
| Institution: | 1. Department of Information Management and Finance, Institute of Information Management, National Chiao Tung University, Hsinchu, Taiwan, ROC;2. Department of Industrial and Systems Engineering, North Carolina State University, USA;3. School of Economics and Management, Southwest Jiaotong University, Chengdu 610031, China;1. Institute of Information Technology, University of Dhaka, Dhaka, Bangladesh;2. Department of Computer Science and Engineering, Independent University, Bangladesh;3. Department of Computer Science and Engineering, University of Dhaka, Dhaka, Bangladesh;4. Department of Computer Engineering, Kyung Hee University, Gyeonggido, South Korea |
| |
| Abstract: | The aim of this paper is to develop a Parallel Scatter Search metaheuristic for solving the Feature Subset Selection Problem in classification. Given a set of instances characterized by several features, the classification problem consists of assigning a class to each instance. Feature Subset Selection Problem selects a relevant subset of features from the initial set in order to classify future instances. We propose two methods for combining solutions in the Scatter Search metaheuristic. These methods provide two sequential algorithms that are compared with a recent Genetic Algorithm and with a parallelization of the Scatter Search. This parallelization is obtained by running simultaneously the two combination methods. Parallel Scatter Search presents better performance than the sequential algorithms. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
