Evolutionary Multivariate Kernal Svm Prediction Method for Classification
K.Geetha
Dr.K.GEETHA, Department of Computer Applications, Sri Krishna Adthiya College of Arts and Science, Coimbatore, Tamil Nadu, India.
Manuscript received on May 16, 2020. | Revised Manuscript received on May 30, 2020. | Manuscript published on June 10, 2020. | PP: 638-640 | Volume-9 Issue-8, June 2020. | Retrieval Number: D1923029420/2020©BEIESP | DOI: 10.35940/ijitee.D1923.069820
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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: Thyroid disorders are common among the world wide population. This disorders posses’ significant problems among Indians. Research studies shows that nearly 32% of Indian population suffers from various thyroid disorders. This paper deals with the thyroid data set which in turn classify into three groups as hyper thyroidisim, hypothyroidism and normal. The American Thyroid Association reported twelve percent of their citizens suffer from thyroidism in which 60% population are unaware of their condtions.. Above statistics implies the classification of thyroid disorder is crucial in global perspective too. The thyroid data set are collected from UCI repository and it is multivariate type with 21 attributes. With the 21 attributes only 10 attributes are selected based on their rank. Hybrid Differential Evolution Kernel Based SVM algorithm is used to classify the data set. It takes around 30 epochs to stabilize the errors. The classification accurancy is observed to be 67.97%.
Keywords: Curse of Dimensionality, Classification, Evolutionary Algorithm, Multivariate Data Type, Thyroid Data Set
Scope of the Article: Classification