SVM and KNN Based SGO Feature Selection Algorithm for Breast Cancer Diagnosis
P. Srihari1, D. Lalitha Bhaskari2
1P Sri Hari*, Assistant Professor, Dept of IT, GMRIT, Rajam, India.
2D. Lalitha Bhaskari, Dept of CS & SE, Andhra University College of Engineering (A), Andhra University, Visakhapatnam, India.
Manuscript received on March 16, 2020. | Revised Manuscript received on March 24, 2020. | Manuscript published on March 30, 2020. | PP: 2237-2240 | Volume-8 Issue-6, March 2020. | Retrieval Number: D4428118419/2020©BEIESP | DOI: 10.35940/ijrte.D4428.038620
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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: In diagnosis and prediction systems, algorithms working on datasets with a high number of dimensions tend to take more time than those with fewer dimensions. Feature subset selection algorithms enhance the efficiency of Machine Learning algorithms in prediction problems by selecting a subset of the total features and thus pruning redundancy and noise. In this article, such a feature subset selection method is proposed and implemented to diagnose breast cancer using Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) algorithms. This feature selection algorithm is based on Social Group Optimization (SGO) an evolutionary algorithm. Higher accuracy in diagnosing breast cancer is achieved using our proposed model when compared to other feature selection-based Machine Learning algorithms.
Keywords: Subset Selection, Breast Cancer, Prediction, SGO, PSO, GA, CART, SVM, KNN
Scope of the Article: Algorithm Engineering.