A Feature Selection Method Based on ∩ – Fuzzy Similarity Measures Using Multi Objective Genetic Algorithm
Hassan Nosrati Nahook1, Mahdi Eftekhari2

1Hassan Nosrati Nahook, MSc Computers – AI, Computer Engineering Department, Science and Research Branch, Islamic Azad University, Kerman, Iran .
2Mahdi Eftekhari, Assistant Professor, Department Computer Engineering – Faculty of Engineering – Shahid Bahonar University of Kerman, Iran.
Manuscript received on April 04, 2013. | Revised Manuscript received on April 27, 2013. | Manuscript published on May 05, 2013. | PP: 37-41 | Volume-3, Issue-2, May 2013. | Retrieval Number: E0161081511/2013©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Feature selection (FS) is considered to be an important preprocessing step in machine learning and pattern recognition, and feature evaluation is the key issue for constructing a feature selection algorithm. Feature selection process can also reduce noise and this way enhance the classification accuracy. In this article, feature selection method based on ∩ – fuzzy similarity measures by multi objective genetic algorithm (FSFSM – MOGA) is introduced and performance of the proposed method on published data sets from UCI was evaluated. The results show the efficiency of the method is compared with the conventional version. When this method multi-objective genetic algorithms and fuzzy similarity measures used in CFS method can improve it.
Keywords: Feature Selection, Fuzzy Similarity Measures, Multi Objective Genetic.