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Computational Intelligence Techniques for Classification of Cancer Data
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 132 - Number 10 |
| Year of Publication: 2015 |
| Authors: Kalagotla Satish Kumar, T. Sita Mahalakshmi |
10.5120/ijca2015907607
|
Kalagotla Satish Kumar, T. Sita Mahalakshmi . Computational Intelligence Techniques for Classification of Cancer Data. International Journal of Computer Applications. 132, 10 ( December 2015), 14-21. DOI=10.5120/ijca2015907607
Abstract
This paper presents improved Ant Colony Optimization (ACO) algorithms for data mining. The goal of the algorithms is to extract classification rules from data. The traditional Ant Colony Optimization algorithm is enhanced with genetic operators to develop improved ACO algorithms. The genetic operators like crossover, mutation are used to develop Ant Colony Optimization with Crossover (ACOC), ACO with mutation (ACOM), and ACO with crossover and mutation (ACOCM). The performance of the improved ACO algorithms is compared with traditional ACO. All the algorithms are applied on three different cancer datasets. The results showed that ACO with mutation gave good accuracy when compared with ACO, ACOC, and ACOCM.
References
- Dorigo, M. and Gambardella, L. M. 1997. Ant colony system: a cooperative learning approach to the travelling salesman problem. IEEE transactions on evolutionary computing. 1, 53-66.
- Dorigo, M., Maniezzo, V., Colorni, A. The ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man and Cybernetics-Part B. 26, 1-13.
- Gambardella, L, M. and Dorigo, M. 1996. Solving symmetric and asymmetric tsps by ant colonies. IEEE Conference on Evolutionary Computation. 1-6.
- Parpinelli, R.S., Lopes, H.S. and Freitas, A. A. 2002. Data mining with an ant colony optimization algorithm. IEEE transactions on evolutionary computing. 6, 321-332.
- Ganji, M. F. and Abadeh, M. S. 2011. A fuzzy classification system based on ant colony optimization for diabetes disease diagnosis. Expert systems with applications. 38, 14650-14659.
- Tabakhi, S. and Moradi, P. 2015. Relevance-Redundancy feature selection based on ant colony optimization. Pattern recognition. 48, 2798-2811.
- Liu, B., Abbass, H. A. and McKay, B. 2004. Classification rule discovery with ant colony optimization. IEEE Computational Intelligence Bulletin, 3, 31-35.
- Cordon, O., Herrera, F. and Stutzle, T. 2002. A review on the ant colony optimization metaheuristic: basis, models and new trends. Math ware and Soft Computing. 9, 1-35.
- Martens, D., Backer, M. D., Haesen, R., Vanthienen, J., Snoeck, M. and Baesens, B. 2007.Classification with ant colony optimization. IEEE Transactions on evolutionary computing. 11, 651-665.
- Lichman, M. Breast Cancer Wisconsin (Original) Dataset, UCI Machine Learning Repository [http://archive.ics.uci.edu/ml] last accessed 19th may 2015.
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