Department Of Electronics And Communication Engineering, Bannari Amman Institute Of Technology, Sathyamangalam – 638401, India.
Abstract
The World İs So Advanced And Sophisticated Enough Nowadays. But, Cancer Still Remains As A Deadly Disease İn Many Parts Of The World For All Living Organisms. The İncidence Rate Of Cancer Among Humans Globally İs İncreasing Steadily Day By Day.Among All Forms Of Tumors, Breast Cancer İs A Type Of İllness That Plays A Major Role İn Disturbingthesurvivalrate Of Humans Globally.Thus, There İs A Need To Predict Breast Cancer İn İts Earlier Stage. Thus, The Work İn This Paperis Aided To Design A Robust Classification Model That İnvolves The Use Of A Randomized- Parameter Optimizedmulti-Kernel Support Vector Machine (RPOMK-SVM) Classifier. Before The Stage Of Classification, The Paper Analyzes The Nature Of İnput İn Order To Obtain Promising Results. The Skewness Of The Feature Attributes İs Controlled And Reformed Using Box-Cox Transform.For This Analysis And Evaluation, The Paperemploysthe Breast Cancer Wisconsin (Diagnostic) Database,Which İs A Standard Public Dataset. The Final Results Are Then Compared Against The Existing Algorithms.
Rajaguru, H., & S R, S. C. (2021). Effectiveclassificationframework For Breast Tumorsusing Optimized Multi-Kernel Svm With Controlled Skewness. Int. J. of Aquatic Science, 12(3), 1604-1612.
MLA
Harikumar Rajaguru; Sannasi Chakravarthy S R. "Effectiveclassificationframework For Breast Tumorsusing Optimized Multi-Kernel Svm With Controlled Skewness". Int. J. of Aquatic Science, 12, 3, 2021, 1604-1612.
HARVARD
Rajaguru, H., S R, S. C. (2021). 'Effectiveclassificationframework For Breast Tumorsusing Optimized Multi-Kernel Svm With Controlled Skewness', Int. J. of Aquatic Science, 12(3), pp. 1604-1612.
VANCOUVER
Rajaguru, H., S R, S. C. Effectiveclassificationframework For Breast Tumorsusing Optimized Multi-Kernel Svm With Controlled Skewness. Int. J. of Aquatic Science, 2021; 12(3): 1604-1612.
)