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Computational Intelligence Techniques for Classification of Cancer Data

by Kalagotla Satish Kumar, T. Sita Mahalakshmi
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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

@article{ 10.5120/ijca2015907607,
author = { Kalagotla Satish Kumar, T. Sita Mahalakshmi },
title = { Computational Intelligence Techniques for Classification of Cancer Data },
journal = { International Journal of Computer Applications },
issue_date = { December 2015 },
volume = { 132 },
number = { 10 },
month = { December },
year = { 2015 },
issn = { 0975-8887 },
pages = { 14-21 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume132/number10/23629-2015907607/ },
doi = { 10.5120/ijca2015907607 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:28:59.721469+05:30
%A Kalagotla Satish Kumar
%A T. Sita Mahalakshmi
%T Computational Intelligence Techniques for Classification of Cancer Data
%J International Journal of Computer Applications
%@ 0975-8887
%V 132
%N 10
%P 14-21
%D 2015
%I 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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Index Terms

Computer Science
Information Sciences

Keywords

Data Mining Ant Colony Optimization Classification Crossover Mutation.

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