Feature Selection With Centre of Gravity Method using Ant Colony Optimization
Leena C Sekhar1, R Vijayakumar2, M K Sabu3
1Leena C Sekhar, Department of Computer Application, M E S College, Marampally, Aluva, Ernakulam, Kerala, India.
2R Vijayakumar, School of Computer Science, Mahatma Gandhi University, Kottayam, Kerala, India.
3M K Sabu, Department of Computer Application, Cochin University of Science and Technology, Kochi, Kerala, India.
Manuscript received on 15 August 2019. | Revised Manuscript received on 25 August 2019. | Manuscript published on 30 September 2019. | PP: 695-699 | Volume-8 Issue-3 September 2019 | Retrieval Number: B2887078219/19©BEIESP | DOI: 10.35940/ijrte.B2887.098319
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© The Authors. 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: The high dimensional dataset with irrelevant, redundant and noisy features has much influence on the performance of machine learning problems. In this work, an existing Ant Colony Optimization (ACO) based feature selection algorithm is modified by attaching a dimensionality reduction method as a data pre-processing step. This is achieved by introducing the concept of Centre of Gravity (CoG) of a set of points. After reducing the dimension, the ACO algorithm is used to generate the optimal subset of features. The performance of the proposed algorithm is evaluated using Artificial Neural Network (ANN) classifier. The performance comparison using various dataset shows that the proposed method outperforms the existing ACO based feature selection methods.
Keywords: Ant Colony Optimization, Centre of Gravity, Dimensionality Reduction, Feature Selection
Scope of the Article: Ant Colony Optimization