A Comparative Study of Classification Rule Discovery with Ant Colony Optimization: AntMiner

Authors

  • Bhawna jyoti Computer Science Department, Himachal Pradesh University, Shimla, India
  • Dr. Aman Kumar Sharma Computer Science Department, Himachal Pradesh University, Shimla, India

Keywords:

Ant Colony Optimization, AntMiner, Classification Rules, Rule induction, Sequential covering Strategy

Abstract

Rule based classification is the fundamental and important task of data classification. To discover classification rules, ant colony optimization algorithms are successfully applied that follow a sequential covering approach to build a list of rules. AntMiner Rule Based Classification algorithms are inspired from self- organizing behaviour of ant colonies. In this paper, we presented a study on Ant Colony Optimization Algorithm, AntMiner, c_AntMiner, c_AntMiner2, c_AntMiner PB and  conducted experiments to find predictive accuracy against well-known rule induction algorithms JRIP and PART and results shows that AntMiner and its variants shows comparable as well as better performance in some datasets taken in the experimental study.

References

. G. Chennupati. “Eant-miner: an ensemble ant-miner to improve the aco classification”, arXiv preprint arXiv, pp. 2014 .

. S. P. Rami, H. M. Panchal. “Comparative Analysis of Variations of Ant-Miner by Varying Input Parameters”, International Journal of Computer Applications, vol. 60, issue 3, 2012.

. X. Y. Zhong, Z. Lei, Z, Yu-Fang. “A classification rule mining method using hybrid genetic algorithms” in IEEE Region 10 Conference TENCON, 2004, pp. 207-210.

. B. Suri, S. Singhal.“Implementing ant colony optimization for test case selection and prioritization,” International journal on computer science and engineering, vol. 3, issue 5, pp. 1924-32, May 2011.

. F.E. Otero, A. A. Freitas, C.G. Johnson. A new sequential covering strategy for inducing classification rules with ant colony algorithms. IEEE Transactions on Evolutionary Computation. vol.17, issue 1, pp. 64-76. Feb 2012.

. R. S. Parpinelli, H.S. Lopes, A. A. Freitas. “Data mining with an ant colony optimization algorithm,” IEEE transactions on evolutionary computation. vol.6, issue. 4, pp. 321-32, Aug 2002.

. M. Dorigo, T. Stutzle. “Ant colony optimization: overview and recent advances,” in Handbook of metaheuristics Springer, pp. 311-351, 2019.

. R. Seidlova, J. Pozivil. “Implementation of Ant Colony Algorithms in Matlab,” Institute of Chemical Technology, Department of Computing and Control Engineering, 2012.

. S. Katiyar, N. Ibraheem, A. Q. Ansari. “Ant colony optimization: a tutorial review,” in Proceedings of the National Conference on Advances in Power and Control, Manav Rachna International University, Faridabad, Haryana, India, Aug 2015,vol. 574.

. H. Li, C. P. Lam. “Software Test Data Generation using Ant Colony Optimization,” in International conference on computational intelligence, 2004,pp. 1-4.

. S. Roy, S. S. Chaudhuri. “Bio-inspired ant algorithms: A review,” International Journal of Modern Education and Computer Science. vol. 5, issue. 4, May 2013.

. B. C. Mohan, R. Baskaran. “A survey: Ant Colony Optimization based recent research and implementation on several engineering domain,” Expert Systems with Applications, vol. 39, issue. 4, pp. 4618-27, Mar 2012.

. J. Brookhouse, F.E. Otero. “Discovering regression rules with ant colony optimization,” in Proceedings of the Companion Publication of the 2015 ACOGEC, 2015, pp. 1005-1012. ACM.

. R. S. Parpinelli, H. S. Lopes, A. A. Freitas. “An ant colony based system for data mining: applications to medical data,” in Proceedings of the 3rd ACOGEC, 2001, pp. 791-797.

. M. Medland, F. E. Otero, A. A. Freitas. “Improving the cAnt-Miner PB classification algorithm,” in international Conference on Swarm Intelligence, Sep 2012, pp. 73-84. Springer.

. D. Martens, D. Backer, R. Haesen, J. Vanthienen, M. Snoeck, B. Baesens. “Classification with ant colony optimization,” IEEE Transactions on Evolutionary Computation. vol. 11, issue. 5, Oct 2007, pp. 651-65.

. F. E. Otero. “MYRA: a Java ant colony optimization framework for classification algorithms,” in proceedings of the Genetic and Evolutionary Computation Conference Companion, Jul 2017, pp. 1247-1254. ACM.

. K. M. Salama, A. M. Abdelbar, F. E. Otero, A. A. Freitas. “Utilizing multiple pheromones in an ant-based algorithm for continuous-attribute classification rule discovery,” Applied Soft Computing, Jan 2013, vol. 13, issue 1, pp. 667-75.

. Otero, A. A. Freitas. “Improving the interpretability of classification rules discovered by an ant colony algorithm,” in Proceedings of the 15th annual conference on GEC, ACM, 2013, pp. 73-80.

. A. Helal, F. E. Otero. “A mixed-attribute approach in ant-miner classification rule discovery algorithm,” in Proceedings of the Genetic and Evolutionary Computation Conference, 2016, pp. 13-20, ACM.

. J. Brookhouse, F. E. Otero. “Using an Ant Colony Optimization Algorithm for Monotonic Regression Rule Discovery,” in Proceedings of the GEC, Jul 2016, pp. 437-444. ACM.

. A. Amuthan, K. D. Thilak. “Improved Ant Colony Algorithms for Eliminating Stagnation and Local Optimum Problem—A Survey,” in proceedings of ICTACC, Apr 2017, pp. 97-101, IEEE.

. M. Dorigo, V. Maniezzo, A. Colorni. “Ant system: optimization by a colony of cooperating agents,” IEEE Transactions on Systems, man and cybernetics, Part B: Cybernetics. Feb 1996, vol. 26, issue. 1, pp. 129-41.

. A. Helal, F. E. Otero. “Automatic design of ant-miner mixed attributes for classification rule discovery,” in Proceedings of the Genetic and Evolutionary Computation Conference, Jul 2017, pp. 433-440. ACM.

. A. Helal, J. Brookhouse, F. E. Otero. “Archive-Based Pheromone Model for Discovering Regression Rules with Ant Colony Optimization,” IEEE Congress on Evolutionary Computation (CEC), Jul 2018, pp. 1-7.

. W. J. Jiang, Y. H. Xu, Y. S. Xu. “A novel data mining algorithm based on ant colony system,” in International Conference on Machine Learning and Cybernetics, Aug 2005, vol. 3, pp. 1919-1923, IEEE.

. H. N. Al-Behadili, K. R. Ku-Mahamud, R. Sagban. “Rule pruning techniques in the ant-miner classification algorithm and its variants: A review,” in IEEE Symposium on ISCAIE, Apr 2018, pp. 78-84, IEEE.

. V. K. Panchal, P. Singh, A. Narula, A. Mishra. “Review on ant miners,” in World Congress on Nature & Biologically Inspired Computing, Dec 2009, pp. 1641-1644, IEEE.

. Yang XS. “Nature-Inspired Metaheuristic Algorithms,” Second Edition. Luniver Press, 2010.

. Shah C, Jivani AG. “Comparison of data mining classification algorithms for breast cancer prediction,” in Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT), Jul 2013, pp. 1-4. IEEE.

. A. Colorni, M. Dorigo, F. Maffioli, V. Maniezzo, G. I. Righini, M. Trubian. “Heuristics from nature for hard combinatorial optimization problems,” International Transactions in Operational Research. 1996, vol. 3, issue. 1, pp. 1-21.

. K. M. Salama, A. M. Abdelbar, F. E. Otero. “Investigating evaluation measures in ant colony algorithms for learning decision tree classifiers,” in IEEE symposium series on computational intelligence, 2015, pp. 1146-1153.

. S. H. Ripon, “Rule Induction and Prediction of Chronic Kidney Disease Using Boosting Classifiers, Ant-Miner and J48 Decision Tree,” International Conference on ECCE, Feb 2019, pp. 1-6, IEEE.

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Published

2019-08-07

How to Cite

jyoti, B., & Kumar Sharma, D. A. . (2019). A Comparative Study of Classification Rule Discovery with Ant Colony Optimization: AntMiner. International Journal of Computer (IJC), 34(1), 119–128. Retrieved from https://www.ijcjournal.org/index.php/InternationalJournalOfComputer/article/view/1446

Issue

Vol. 34 No. 1 (2019)

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Articles

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