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Feature Selection based Sequential Minimal Optimization (SMO) Classifier for Heart Disease Classification
http://hdl.handle.net/20.500.12678/0000006181
http://hdl.handle.net/20.500.12678/000000618193dc8151-7fba-40dc-9ec6-cad7afcdec5d
0a8e881c-7ea5-4ef1-89b6-a0fb22cb6b0d
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Feature Selection based Sequential Minimal Optimization (SMO) Classifier for Heart Disease Classification.pdf (201 Kb)
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Journal article | ||||||
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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 |