English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In this paper ensemble learning based feature selection and classifierensemble model is proposed to improve classification accuracy. The hypothesisis that good feature sets contain features that are highly correlated with theclass from ensemble feature selection to SVM ensembles which can be achieved onthe performance of classification accuracy. The proposed approach consists oftwo phases: (i) to select feature sets that are likely to be the supportvectors by applying ensemble based feature selection methods; and (ii) toconstruct an SVM ensemble using the selected features. The proposed approachwas evaluated by experiments on Cardiotocography dataset. Four featureselection techniques were used: (i) Correlation-based, (ii) Consistency-based,(iii) ReliefF and (iv) Information Gain. Experimental results showed that usingthe ensemble of Information Gain feature selection and Correlation-basedfeature selection with SVM ensembles achieved higher classification accuracythan both single SVM classifier and ensemble feature selection with SVMclassifier.

Enhanced Classification Accuracy for Cardiotocogram Data with Ensemble Feature Selection and Classifier Ensemble