A RELATIVE INVESTIGATORY ANALYSIS ON ANT COLONY ALGORITHM AND GENETIC ALGORITHM FOR FEATURE SELECTION
DOI:
https://doi.org/10.53555/eijse.v6i3.73Keywords:
GA, ACO, Selection, Crossover, Mutation, ElitismAbstract
Feature subset selection is used as a common technique in data pre-processing for pattern recognition, machine learning and data mining has attracted much attention in recent years. A good feature selection method can reduce the cost of feature measurement and increase classifier efficiency and classification accuracy. One approach in the feature selection area is employing population based optimization algorithms such as Genetic Algorithm (GA)-based method and Ant Colony Optimization (ACO) based method. In this paper ant colony optimization algorithm (ACO) is compared to Genetic algorithm (GA) using feature selection. Finally a comparative study is done to know the pros and cons of the work done.
References
. H. Liu, L. Yu, "Toward Integrating Feature Selection Algorithms for Classification and Clustering", IEEE Trans. Know l. Data Eng vol.17 (4) pp.491-502, 2005.
. M.Dorigo and T.,"Stutzle,Ant Colony Optimization, The MIT Press, pp.21-22, 2004.
. N. Jain and J. P. Singh, "Modification of Ant Algorithm for Feature Selection", International conference on control, automation, communication and energy conservation, 2009.
. S. Kashef, H. Nezamabadi-pour, "A new feature selection algorithm based on binary ant colony optimization", Conference on Information and Knowledge Technology (IKT),2013
. F. Tan, X. Fu, Y. Zhang, and A. G. Bourgeois, A genetic algorithm-based method for feature subset selection, Soft Computing, vol. 12, no. 2, pp. 111-120, (2008).
. M. E. ElAlami, A _lter model for feature subset selection based on genetic algorithm, Knowledge-Based Systems, vol. 22, no. 5, pp. 356-362, (2009).
. Yang and V. Honavar, Feature subset selection using a genetic algorithm, Intelligent System and Their Applications IEEE, vol. 13, no. 2, pp. 44-49, (1998).
. H. Liu and L. Yu, Toward integrating feature selection algorithms for classification and clustering, IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 4, pp. 491502, (2005).
. T. M. Hamdani, J.-M. Won, A. M. Alimi, and F. Karray, Multi-objective Feature Selection with NSGA II, Adaptive and Natural Computing Algorithms, Springer Berlin Heidelberg, pp. 240-247 (2007).
. A.Freitas and C. A. Kaestner, Attribute selection with a multi-objective genetic algorithm, Proc. of the 16th Brazilian Symposium on Artificial Intelligence, (2002).
. C.-M. Wang and Y.-F. Huang, Evolutionary-based feature selection approaches with new criteria for data mining: A case study of credit approval data, Expert Systems with Applications, vol. 36, no. 3, pp. 5900-5908, (2009).
. G. L. Pappa, A. A. Freitas, and C. A. Kaestner, A multi-objective genetic algorithm for attribute selection, Proceedings of the 4th International Conference on Recent Advances in Soft Computing (RASC-2002), pp. 116-121, (2002).
. C. Emmanouilidis, A. Hunter, and J. MacIntyre, A multi-objective evolutionary setting for feature selection and a commonality-based crossover operator, Proceedings of the 2000 Congress on Evolutionary Computation, vol. 1, pp. 309-316, (2000).
. L. S. Oliveira, M. Morita, and R. Sabourin, Feature Selection for Ensembles Using the MultiObjective Optimization Approach, Multi-Objective Machine Learning, Y. J. Dr, Ed. Springer Berlin Heidelberg, pp. 49-74, (2006).
. M.Venkatadri and K.Srinivasa Rao, A Multi-objective Genetic Algorithm for Feature Selection in Data Mining, International Journal of Computer Science and Information Technologies, vol. 1, no. 5, pp. 443-448, (2010).
. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, A fast and elitist multi-objective genetic algorithm: NSGAII, IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182197, (2002).
. A. Konak, D. W. Coit, and A. E. Smith, Multiobjective optimization using genetic algorithms: A tutorial, Reliability Engineering and System Safety, vol. 91, no. 9, pp. 992-1007, (2006).
. M. Dorigo and C. Blum, Ant colony optimization theory: A survey, Theoretical Computer Science, 344, pp. 243-278, 2005.
. M. Dorigo, Optimization, Learning and Natural Algorithms, Ph.D. dissertation, Department of di Electronica, Politecnico di Milano. Italy, 1992.
. M. Dorigo, Learning by Probabilistic Boolean Networks, in Proc. IEEE International conf. Neural Networks, pp. 887-891, 1994.
Downloads
Published
Issue
Section
License
Copyright (c) 2020 EPH - International Journal of Science And Engineering (ISSN: 2454 - 2016)
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
