Evolutionary-based feature selection approaches with new criteria for data mining: A case study of credit approval data |
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| Authors: | Chia-Ming Wang Yin-Fu Huang |
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| Affiliation: | 1. Graduate School of Engineering Science and Technology, National Yunlin University of Science and Technology, 123 University Road, Section 3, Touliou, Yunlin 640, Taiwan, ROC;2. Graduate School of Computer Science and Information Engineering, National Yunlin University of Science and Technology, 123 University Road, Section 3, Touliou, Yunlin 640, Taiwan, ROC;3. Department of Computer and Communication Engineering, National Yunlin University of Science and Technology, 123 University Road, Section 3, Touliou, Yunlin 640, Taiwan, ROC;1. Department of Computer Science & Engineering, Tezpur University, Napaam, Tezpur 784028, Assam, India;2. Department of Computer Science, University of Colorado at Colorado Springs, CO 80933-7150, USA;1. Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Saveh, Iran;2. Department of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran;3. Department of Information Technology Management, Science and Research Branch, Islamic Azad University, Tehran, Iran;1. School of Science, Beijing Jiaotong University, Beijing 100044, China;2. Department of Basic Courses, Beijing Union University, Beijing 100101, China |
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| Abstract: | In this paper, the feature selection problem was formulated as a multi-objective optimization problem, and new criteria were proposed to fulfill the goal. Foremost, data were pre-processed with missing value replacement scheme, re-sampling procedure, data type transformation procedure, and min-max normalization procedure. After that a wide variety of classifiers and feature selection methods were conducted and evaluated. Finally, the paper presented comprehensive experiments to show the relative performance of the classification tasks. The experimental results revealed the success of proposed methods in credit approval data. In addition, the numeric results also provide guides in selection of feature selection methods and classifiers in the knowledge discovery process. |
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