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Feature Selection based Sequential Minimal Optimization (SMO) Classifier for Heart Disease Classification

http://hdl.handle.net/20.500.12678/0000006181
93dc8151-7fba-40dc-9ec6-cad7afcdec5d
0a8e881c-7ea5-4ef1-89b6-a0fb22cb6b0d
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Feature Feature Selection based Sequential Minimal Optimization (SMO) Classifier for Heart Disease Classification.pdf (201 Kb)
Publication type
Journal article
Upload type
Publication
Title
Title Feature Selection based Sequential Minimal Optimization (SMO) Classifier for Heart Disease Classification
Language en
Publication date 2019-06-03
Authors
Aung Nway Oo
Khin Thuzar Win
Description
Feature Selection is one of the pre-processing steps in machine learning. Feature Selection effectively reduced the dimensionality of dataset, removing irrelevant and redundant feature. In this paper, we proposed a Correlation based Feature subset Selection (CFS) based Sequential Minimal Optimization (SMO) classifier for heart disease classification. Experimental results of CFS-SMO and SMO are compared by using heart disease dataset from UCI. Comparative results show that the proposed CFS-SMO classifier is better than SMO classifier.
Keywords
Feature selection, Classification, Correlation and SMO
Journal articles
UJSER
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