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

TL;DR

This paper introduces an ensemble learning approach combining feature selection and classifier ensemble to significantly improve cardiotocogram data classification accuracy, outperforming traditional methods.

Contribution

It proposes a novel two-phase ensemble method that integrates multiple feature selection techniques with SVM ensembles for enhanced accuracy.

Findings

Ensemble of Information Gain and Correlation-based feature selection yields highest accuracy.

The proposed method outperforms single SVM classifiers.

Using ensemble feature selection improves classification performance.

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.