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Computational Intelligence Techniques for Classification of Cancer Data

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2015
Authors:
Kalagotla Satish Kumar, T. Sita Mahalakshmi
10.5120/ijca2015907607

Kalagotla Satish Kumar and Sita T Mahalakshmi. Article: Computational Intelligence Techniques for Classification of Cancer Data. International Journal of Computer Applications 132(10):14-21, December 2015. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

@article{key:article,
	author = {Kalagotla Satish Kumar and T. Sita Mahalakshmi},
	title = {Article: Computational Intelligence Techniques for Classification of Cancer Data},
	journal = {International Journal of Computer Applications},
	year = {2015},
	volume = {132},
	number = {10},
	pages = {14-21},
	month = {December},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}
}

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

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Keywords

Data Mining, Ant Colony Optimization, Classification, Crossover, Mutation.