Breast Cancer Classification using Nature-inspired Algorithm
J Daphy Louis Lovenia1, S. Ezhilin Freeda2, D. Darling Jemima3, Nathaniel Christopher4
1Dr. Daphy Louis Lovenia, Department of Mathematics, Karunya University Karunya Nagar Coimbatore (Tamil Nadu), India.
2S. Ezhilin Freeda, Assistant Professor, Department of Computer Science and Engineering, Sri Ramakrishna Engineering College, Coimbatore (Tamil Nadu), India.
3D. Darling Jemima, Assistant Professor, Sri Krishna College of Technology, Coimbatore (Tamil Nadu), India.
4Nathaniel Christopher, Quantitative Researcher-Algorithmic Trading New York, New York, United States.
Manuscript received on 13 October 2019 | Revised Manuscript received on 22 October 2019 | Manuscript Published on 02 November 2019 | PP: 1024-1027 | Volume-8 Issue-2S11 September 2019 | Retrieval Number: B11720982S1119/2019©BEIESP | DOI: 10.35940/ijrte.B1172.0982S1119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Breast Cancer is one among the dangerous ailments that roots up of deaths among women worldwide. Lots of risk factors have been identified through research though the exact reasons of breast cancer are not yet fully understood The Artificial Immune Recognition System classifier helps to classify the type of breast cancer on Wisconsin dataset which provides accurate prediction of the classes of breast cancer. i.e, Benign and Malignant. This system focuses on supervised classification with the help of clonal selection algorithm, Hierarchical Learning Vector Quantization, Multipass Self Organizing Map. The goal of this system can implement the algorithm to classify the cancer accurately and to compare the error rate, f-measure with previous classification algorithms.
Keywords: Classification, Clonal Rate, Affinity Recognition Balls, Hierarchical LVQ, Multipass SOM.
Scope of the Article: Classification