  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.
