CFP last date
22 April 2024
Call for Paper
May Edition
IJCA solicits high quality original research papers for the upcoming May edition of the journal. The last date of research paper submission is 22 April 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

PSO based Swarm Intelligence Technique for Multi- Objective Classification Rule Mining

by Anil Kumar Mishra
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 137 - Number 2
Year of Publication: 2016
Authors: Anil Kumar Mishra
10.5120/ijca2016908697

Anil Kumar Mishra . PSO based Swarm Intelligence Technique for Multi- Objective Classification Rule Mining. International Journal of Computer Applications. 137, 2 ( March 2016), 18-22. DOI=10.5120/ijca2016908697

@article{ 10.5120/ijca2016908697,
author = { Anil Kumar Mishra },
title = { PSO based Swarm Intelligence Technique for Multi- Objective Classification Rule Mining },
journal = { International Journal of Computer Applications },
issue_date = { March 2016 },
volume = { 137 },
number = { 2 },
month = { March },
year = { 2016 },
issn = { 0975-8887 },
pages = { 18-22 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume137/number2/24247-2016908697/ },
doi = { 10.5120/ijca2016908697 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:37:15.961714+05:30
%A Anil Kumar Mishra
%T PSO based Swarm Intelligence Technique for Multi- Objective Classification Rule Mining
%J International Journal of Computer Applications
%@ 0975-8887
%V 137
%N 2
%P 18-22
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Today’s real world faces different kinds of complex optimization problems. The existing methodologies can’t cope of with such complex problems. This paper presents classification rule mining as a multi-objective problem rather than a single objective one. Multi-Objective optimization is a challenging area and focus for research. Here two modern domains of research are discussed one is swarm intelligence and other is data mining. In this paper PSO is taken as taken as a swarm intelligence algorithm and classification rule mining is taken as the problem domain. In classification rule discovery, classifiers are designed through the following two phases: rule extraction and rule selection. In the rule extraction phase, a large number of classification rules are extracted from training data. This phase is based on two rule evaluation criteria: support (coverage) and confidence. An association rule mining technique is used to extract classification rules satisfying pre-specified threshold values of minimum support (coverage) and confidence. In second phase, a small number of rules are targeted from the extracted rules to design an accurate and compact classifier. In this paper, I used PSO for multiple objective rule selection to maximize the accuracy of the rule sets and minimize their complexity.

References
  1. Yong Rui, Thomas S. Huang, Michael Ortega and Sharad Mehrotra, “Relevance Feedback: A Power Tool for Interactive Content-Based Image Retrieval”, IEEE Transactions on Circuits and Video Technology, Vol. 8, No. 5, pp. 644-655, 1998
  2. Mishra Anil Kumar, Das Madhabananda and Panda T. C., “A Hybrid Swarm Intelligence Optimization for Benchmark Models by Blending PSO with ABC”, International Review on Modelling & Simulations, Vol. 6, No., pp. 291, 2013.
  3. Anil Kumar Mishra, Madhabananda Das and T. C. Panda, “Hybrid Swarm Intelligence Technique for CBIR Systems” IJCSI International Journal of Computer Science Issues, Vol. 10, No 2, pp. 6-11, March 2013.
  4. Anil Kumar Mishra, Artificial Bee Colony Based Swarm Optimization Technique for Content-Based Image Retrieval System, KIIT University, (2014), Bhubaneswar, Odisha.
  5. Sasisekharan, R., Seshadri, V., & Weiss, S. M. (1996). Data mining and forecasting in large-scale telecommunication networks. IEEE Expert: Intelligent Systems and Their Applications , 11 (1), 37-43.
  6. Shao, L. S., & Fu, G. X. (2008). Disaster prediction of coal mine gas based on data mining. Journal of Coal Science and Engineering , 14 (3), 458-463.
  7. Pei, J., Upadhyaya, S. J., Farooq, F., & Govidaraju, V. (2004). Data mining for intrusion detection: Techniques, applications, and systems. Proceedings of the 20th International Conference on Data Engineering. IEEE Computer Society.
  8. Wong, S., & Li, C. S. (Eds.). (2006). Life Science Data Mining. World Scientific.
  9. SAS Institute White Paper. (2001). Retrieved from Data Mining in the Insurance Industry: http:// www.sas.com/products/miner/index.html.
  10. Gutierrez, F. J., Lerma-Rascon, M. M., Salgado-Garza, L. R., & Cantu, F. J. (2002). Biometrics and Data Mining: Comparision of data mining-based keystroke dynamics methods for identity verification. Proceedings of the Second Mexican International Conference on Artificial Intelligence, Lecture Notes in Computer Science: Advances in Artificial Intelligence,. 2313, pp. 460-469. London, UK: Springer-Verlag.
  11. Cano, J. R., Herrera, F., & Lozano, M. (2006). On the combination of evolutionary algorithms and stratified strategies for training set selection in data mining. Applied Soft Computing , 6, 323-332.
  12. Cano, J. R., Herrera, F., & Lozano, M. (2005). Stratification for scaling up evolutionary protoype selection. Pattern Recognition Letters , 26, 953-963.
  13. Coenen, F., & Leng, P. (2005). Obtaining best parameter values for accurate classification. Proceedings of 5th IEEE Conference on Data Mining, (pp. 549-552).
  14. Coenen, F., Leng, P., & Zhang, L. (2005). Threhold tuning for improved classification association rule mining. Lecture Notes in Artificial Intelligence, Advances in Knowledge Discovery and Data Mining – PAKDD 2005, 3518, pp. 216-225. Berlin.
  15. Bayardo Jr., R. J., & Agrawal, R. Mining the most interesting rules. Proceedings of 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (pp. 145-153).
  16. de la Iglesia, B., Reynolds, A., & Rayward-Smith, V. J. (2005). Developments on a Multi-Objective Metaheuristic (MOMH) Algorithm for Finding Interesting Sets of Classification Rules. Evolutionary Multi Criterion Optimization EMO 2005,Lecture Notes in Computer Science. 3410, pp. 826-840. Berlin: springer.
  17. Ishibuchi, H., & Nojima, Y. (2005). Accuracy-Complexity Tradeoff Analysis by Multiobjective Rule Seleciton. Workshop on Computational Intelligence in Data Mining (pp. 39-48). ICDM.
  18. Bergh, F. V., & Engelbrecht, A. P. (2004). A Cooperative Approach to Particle Swarm Optimization. IEEE Transaction on Evolutionary Computation , 8 (3), 225-239.
  19. Kennedy, J., & Eberhart, R. C. (1995). Particle Swarm Optimization. Proceedings of the IEEE International Conference on Neural Networks, (pp. 1942-1948). Perth, Australia.
  20. Coello, C. A., Pulido, T., & Lechuga, M. S. (2004). Handling Multiple Objectives with Particle Swarm Optimization. IEEE Transactions on Evolutionary Computation , 8 (3), 256-279.
  21. Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithm. Chichester: John Willey & Sons.
  22. Ghosh, A., & Nath, B. T. (2004). Muli-Objective Rule Mining using Genetic Algorithms. Information Sciences , 163, 123-133.
  23. Kaya, M. (2006). Multi-Objective Genetic Algorithm based Approaches for Mining Optimized Fuzzy Association Rules. Soft Computing , 10, 578-586.
Index Terms

Computer Science
Information Sciences

Keywords

Classification multi-objective optimization particle swarm optimization multi-objective classification problem pattern recognition data mining.

Powered by PhDFocusTM