TITLE:
Enhanced Classification Accuracy for Cardiotocogram Data with Ensemble Feature Selection and Classifier Ensemble
AUTHORS:
Tipawan Silwattananusarn, Wanida Kanarkard, Kulthida Tuamsuk
KEYWORDS:
Classification, Feature Selection, Support Vector Machines, Ensemble Learning, Classification Accuracy
JOURNAL NAME:
Journal of Computer and Communications,
Vol.4 No.4,
March
25,
2016
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.