2012 ©
             Publication
Journal Publication
Title of Article Enhanced Classification Accuracy for Cardiotocogram Data with Ensemble Feature Selection and Classifier Ensemble 
Date of Acceptance 25 March 2016 
Journal
     Title of Journal Journal of Computer and Communications 
     Standard OTHER () 
     Institute of Journal Scientific Research Publishing Inc. 
     ISBN/ISSN 2327-5227 
     Volume 2016 
     Issue
     Month 4
     Year of Publication 2016 
     Page 20-35 
     Abstract In this paper ensemble learning based feature selection and classifier ensemble model is proposed to improve classification accuracy. The hypothesis is that good feature sets contain features that are highly correlated with the class from ensemble feature selection to SVM ensembles which can be achieved on the performance of classification accuracy. The proposed approach consists of two phases: (i) to select feature sets that are likely to be the support vectors by applying ensemble based feature selection methods; and (ii) to construct an SVM ensemble using the selected features. The proposed approach was evaluated by experiments on Cardiotocography dataset. Four feature selection techniques were used: (i) Correlation-based, (ii) Consistency-based, (iii) ReliefF and (iv) Information Gain. Experimental results showed that using the ensemble of Information Gain feature selection and Correlation-based feature selection with SVM ensembles achieved higher classification accuracy than both single SVM classifier and ensemble feature selection with SVM classifier. 
     Keyword Classification, Feature Selection, Support Vector Machines, Ensemble Learning, Classification Accuracy 
Author
547080012-3 Miss TIPAWAN SILWATTANANUSARN [Main Author]
Humanities and Social Sciences Doctoral Degree

Reviewing Status มีผู้ประเมินอิสระ 
Status ตีพิมพ์แล้ว 
Level of Publication ชาติ 
citation false 
Part of thesis true 
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