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Rule Discovery for Binary Classification Problem using ACO based Antminer

by Sanjeev Gupta, Sanjeev Bhardwaj
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 74 - Number 7
Year of Publication: 2013
Authors: Sanjeev Gupta, Sanjeev Bhardwaj
10.5120/12898-9806

Sanjeev Gupta, Sanjeev Bhardwaj . Rule Discovery for Binary Classification Problem using ACO based Antminer. International Journal of Computer Applications. 74, 7 ( July 2013), 19-23. DOI=10.5120/12898-9806

@article{ 10.5120/12898-9806,
author = { Sanjeev Gupta, Sanjeev Bhardwaj },
title = { Rule Discovery for Binary Classification Problem using ACO based Antminer },
journal = { International Journal of Computer Applications },
issue_date = { July 2013 },
volume = { 74 },
number = { 7 },
month = { July },
year = { 2013 },
issn = { 0975-8887 },
pages = { 19-23 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume74/number7/12898-9806/ },
doi = { 10.5120/12898-9806 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:41:37.878902+05:30
%A Sanjeev Gupta
%A Sanjeev Bhardwaj
%T Rule Discovery for Binary Classification Problem using ACO based Antminer
%J International Journal of Computer Applications
%@ 0975-8887
%V 74
%N 7
%P 19-23
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Data mining can be performed by number of ways. Classification is one of them. Classification is a data mining technique that assigns items to a predefined categories or classes or labels. The aim of classification is to predict the target class for the inputted data. On the other hand biology inspired algorithms such as Genetic Algorithms (GA) and Swarm based approaches like Particle Swarm Optimization (PSO) and Ant Colonies Optimization (ACO) were used in solving many data mining problems and currently the most prominent choice in the area of swarm intelligence. In this paper binary classification is considered as an area of problem and a modified AntMiner is used to solve the problem. The basic algorithm of AntMiner has been modified with a different classification accuracy function.

References
  1. A. C. Tessmer, "What to learn from near misses: an inductive learning approach to credit risk assessment," Decision Sciences, Vol. 28, No. 1, pp. 105-120, 1997.
  2. Abraham, A. , Grosan, C. , Ramos V. : "Swarm Intelligence in Data Mining". Studies in Computational Intelligence, vol. 34, (2006).
  3. Breiman, L. , Friedman, J. H. , Olshen, R. A. , and Stone, C. J. Classification and Regression Trees. Belmont, CA: Wadsworth, 1984.
  4. Bremner D, Demaine E, Erickson J, Iacono J, Langerman S, Morin P, Toussaint G (2005). "Output-sensitive algorithms for computing nearest-neighbor decision boundaries". Discrete and Computational Geometry 33(4):593– 604. doi:10. 1007/s00454-004-1152-0.
  5. Clark P, Boswell R, "Rule Induction with CN2: Some Recent Improvements". in Proceedings of the Fifth European Conference on Machine Learning, pages 151-163. Berlin, Springer-Verlag, 1991.
  6. D. Martens, M. de Backer, R. Haesen, J. Vanthienen, M. Snoeck, and B. Baesens, "Classification with ant colony optimization," IEEE Transactions on Evolutionary Computation, Vol. 11, No. 5. Oct. 2007
  7. Domingos, Pedro & Michael Pazzani (1997) "On the optimality of the simple Bayesian classifier under zero-one loss". Machine Learning, 29:103–137.
  8. Dorigo, M. , Colorni, A. , Maniezzo, V. : "The Ant System: Optimization by a colony of cooperating agents". IEEE Transactions on Systems, Man, and Cybernetics-Part B. , vol. 26, pp. 29–41 (1996).
  9. Dorigo, M. , Di Caro, G. : The ant colony optimization meta-heuristic, in New Ideas in Optimization. McGraw-Hill. , p. 11, (1999).
  10. Dorigo, M. , Maniezzo, V. , Colorni, A. , "Positive Feedback as a Search Strategy, Technical report". Dipartimento di Elettronica, pp. 91-016 (1991).
  11. Dorigo, M. : "Optimization Learning and Natural Algorithms, Ph. D. thesis (in Italian)". Dipartimento di Elettronica, Politecnico di Milano. (1992).
  12. http://www. ics. uci. edu/mlearn/MLRepository. html
  13. http://www. joinville. udesc. br
  14. J. Han, and M. Kamber, Data Mining: Concepts and Techniques, 2nd ed. , Morgan, Kaufmann Publishers, 2006.
  15. J. Pesce, "Stanching hospitals, "Financial hemorrhage with information technology," Health Management Technology, Vol. 24, No. 8, pp. 6-12, 2003.
  16. J. R . Quinlan, C4. 5: Programs for Machine Learning. Los Altos, California: Morgan Kauffman, 1994.
  17. K. Salama and A. Abdelbar, "Extensions to the Ant-Miner Classification Rule Discovery Algorithm," in Proceedings of the 7th International Conference on Swarm Intelligence (ANTS 2010), Lecture Notes in Computer Science 6234, 2010, pp. 167–178.
  18. K. Salama, A. Abdelbar, and A. Freitas, "Multiple pheromone types and other extensions to the ant-miner classification rule discovery algorithm," Swarm Intelligence, vol. 5, no. 3-4, pp. 149–182, 2011.
  19. Liu, H. A. Abbass, and B. McKay, "Classification rule discovery with ant colony optimization," in Proceedings of IEEE/WIC International Conference on Intelligent Agent Technology, 2004, pp. 83–88.
  20. Liu, H. A. Abbass, and B. McKay, "Classification rule discovery with ant colony optimization," IEEE Computational Intelligence Bulletin, Vol. 3, No. 1, Feb. 2004.
  21. Liu, H. A. Abbass, and B. McKay, "Density-based heuristic for rule discovery with ant-miner," in Proceedings of 6th Australia-Japan Joint Workshop on Intelligent Evolutionary Systems. Canberra, Australia, 2002.
  22. M. J. Berry, and G. Linoff. Data Mining Techniques for Marketing, Sales, and Customer upport. New York: John Wiley, 1997.
  23. N. Holden, and A. A. Freitas, "A hybrid PSO/ACO algorithm for classification,"in Proceeding of GECCO-2007 Workshop on Particle Swarms: The Second Decade, ACM Press, New York, 2007.
  24. Parpinelli, R. S. , Lopes, H. S. , Freitas, A. : ?Data mining with an ant colony optimization algorithm?. IEEE Transactions on Evolutionary Computation, vol. 6, pp. 321–332 (2002).
  25. R. E. Schapire, and Y. Singer, "Improved boosting algorithms using confidence-rated predictions," Machine Learning, Vol. 37(3), pp. 297–336, 1999.
  26. W. Ceusters, "Medical natural language understanding as a supporting technology for data mining in healthcare," Chapter 3 in: Cios K. J. , eds. Medical Data Mining and Knowledge Discovery, Heidelberg: Springer-Verlag, pp. 32-60, 2000.
  27. W. Cohen, "Fast effective rule induction," in Machine Learning: Proceedings of the Twelfth International Conference (ML95), pp. 852-857, 1995.
  28. Z. Wu and C. Li, "Feature selection for classification using transductive support vector machines", in Feature Extraction, Foundations and Applications, Springer, Verlag, Berlin, 2006.
Index Terms

Computer Science
Information Sciences

Keywords

Ant Colony optimization (ACO) Particle Swarm Optimization (PSO) Classification models

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