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Reseach Article

A Modified ACO for Classification on different Data Set

by Dharmpal Singh, J. Paul Choudhury, Mallika De
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 123 - Number 6
Year of Publication: 2015
Authors: Dharmpal Singh, J. Paul Choudhury, Mallika De
10.5120/ijca2015905379

Dharmpal Singh, J. Paul Choudhury, Mallika De . A Modified ACO for Classification on different Data Set. International Journal of Computer Applications. 123, 6 ( August 2015), 39-50. DOI=10.5120/ijca2015905379

@article{ 10.5120/ijca2015905379,
author = { Dharmpal Singh, J. Paul Choudhury, Mallika De },
title = { A Modified ACO for Classification on different Data Set },
journal = { International Journal of Computer Applications },
issue_date = { August 2015 },
volume = { 123 },
number = { 6 },
month = { August },
year = { 2015 },
issn = { 0975-8887 },
pages = { 39-50 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume123/number6/21967-2015905379/ },
doi = { 10.5120/ijca2015905379 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:11:59.230858+05:30
%A Dharmpal Singh
%A J. Paul Choudhury
%A Mallika De
%T A Modified ACO for Classification on different Data Set
%J International Journal of Computer Applications
%@ 0975-8887
%V 123
%N 6
%P 39-50
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Ant colony optimization algorithms have been applied to many combinatorial optimization problems, ranging from quadratic assignment to protein folding or routing vehicles and a lot of derived methods have been adapted to dynamic problems in real variables, stochastic problems, multi-targets and parallel implementations. It has also been used to classification of the data set based on the attribute. It has been observed that construct solution and pheromone update play an important role in the ACO algorithm. The selection of the pheromone update is based on the construct solution which is further base on the probability function and initial selection. So if the selection of the pheromone done properly then ACO algorithm will terminate in less number of the iteration and it will be produce the good result. It has further observed that difference result have been possible for the different selection of the construct and pheromone on the same data set. Therefore, in this paper an effort has been made to suggest the techniques to select the initial construct and pheromone update for data set and the classification has to be done using the concept of clustering.

References
  1. Thomas STUTZLE and Marco DORIGO “ACO Algorithms for the Traveling Salesman Problem”, John Wiley & Sons, 1999
  2. Wei Zhao ; Coll. of Inf. Technol., JiLin Agric. Univ., Changchun, China ; Xingsheng Cai ; Ying Lan, “A New Ant Colony Algorithm for Solving Traveling Salesman Problem”, Computer Science and Electronics Engineering (ICCSEE), 2012 International Conference, Vol.3, pp. 530-533 23-25 March 2012
  3. Héctor D. Menéndez, Fernando E. B. Otero,  David Camacho, “MACOC: A Medoid-Based ACO Clustering Algorithm” 9th International Conference, ANTS 2014, Brussels, Belgium, September 10-12, 2014. Proceedings, pp. 122-133, 2014.
  4. Xiaoyong Liu, “Ant Colony Optimization Algorithm Based on Dynamical Pheromones for Clustering Analysis”, International Journal of Hybrid Information Technology, Vol.7, No.2 (2014), pp.29-38
  5. K. Ayari, S. Bouktif, G. Antoniol,” Automatic Mutation Test Input Data Generation via Ant Colony,” In the Proceedings of the 9th annual conference on Genetic and evolutionary computation [GECCO], London, England, July 2007.
  6. D.J. Mala and V. Mohan, “IntelligenTester – Software Test Sequence Optimization Using Graph Based Intelligent Search Agent,” In the Proceedings of International Conference on Computational Intelligence and Multimedia Applications [ICCIMA], 2007, pp. 2227.
  7. K. Li, Z. Yang, “Generating Method of Pair-wise Covering Test Data Based on ACO,” In the Proceedings of International Workshop on Educational Technology & International Workshop on Geoscience and Remote Sensing, 2008, pp. 776-779.
  8. D.J. Mala, M. Kamalapriya, R. Shobhana, V.Mohan, “A NonPheromone based Intelligent Swarm Optimization Technique in Software Test Suite Optimization,” In the Proceedings of IAMA,2009.
  9. P. R. Srivastava, K. Baby, and G Raghurama, “An Approach of Optimal Path Generation using Ant Colony Optimization,” In the Proceedings of TENCON 2009, IEEE Press, 2009, pp. 1-6.
  10. K. Li, Z. Zhang, and W. Liu, “Automatic Test Data Generation Based On Ant Colony Optimization,” In the Proceedings of Fifth International Conference on Natural Computation (ICNC), IEEE Press, 2009, pp. 216-219.
  11. W. Ding, J. Kou, K. Li, Z. Yang, “An Optimization Method of Test Suite in Regression Test Model,” In the Proceedings of the 2009 WRI World Congress on Software Engineering (WCSE), IEEE Computer Society Washington, DC, USA, Vol. 4, 2009, pp. 180-183.
  12. M. Chis, ”A Survey of the Evolutionary Computation Techniques for Software Engineering,” 1st Chapter In Chis, M. (Ed.), Evolutionary Computation and Optimization Algorithms in Software Engineering: Applications and Techniques, 2010, pp. 1-12.
  13. R. F. Tavares Neto and M. Godinho Filho, “Literature review regarding Ant Colony Optimization applied to scheduling problems: Guidelines for implementation and directions for future research”, Engineering Applications of Artificial Intelligence, Volume 26 Issue 1, pp 150-161. January, 2013
  14. D. P. Singh, J. P. Choudhury and M. De, “ A Comparative Study on the performance of Soft Computing models in the domain of Data Mining,” International Journal of Advancements in Computer Science and Information Technology, Vol. 1, No. 1, pp. 35-49, September, 2011
  15. D. P. Singh, J. P. Choudhury and M. De, “A Comparative Study to Select a Soft Computing Model for Developing the Knowledge Base of Data mining with Association Rule Formation by Factor Analysis”, International Journal of Artificial Intelligence and Knowledge Discovery, Vol. 3, No. 3, October, 2013
  16. D. P. Singh, J. P. Choudhury and M. De, “A comparative study on the performance of Fuzzy Logic, Bayesian Logic and neural network towards Decision Making”International Journal of Data Analysis Techniques and Strategies (IJDATS), Vol. 4, No. 2, pp. 205-216, April, 2012
  17. D. P. Singh, J. P. Choudhury and M. De, “A Comparative Study to Select a Soft Computing Model for Knowledge Discovery in Data Mining”, International Journal of Artificial Intelligence and Knowledge Discovery, Vol. 2, No. 2, pp. 6-19, April, 2012.
  18. D. P. Singh, J. P. Choudhury and M. De, “A comparative study o n principal component analysis a n d factor analysis for the formation of association rule in datamining domain”, Proceedings of the 2nd International Conference on Mathematical, Computational and Statistical Sciences (MCSS '14), Gdansk, Poland, ISBN: 978-960-474-380-3, pp.442-452, May 15-17, 2014, ISI Index
Index Terms

Computer Science
Information Sciences

Keywords

Data mining soft computing Ant colony optimization Particle swarm optimization fuzzy neural network data mining preprocessing.