论文标题
具有集成功能选择和分类器集合的心形图数据的分类精度增强
Enhanced Classification Accuracy for Cardiotocogram Data with Ensemble Feature Selection and Classifier Ensemble
论文作者
论文摘要
在本文中，提出了基于集合学习的特征选择和分类器集合模型，以提高分类精度。假设是，良好的功能集包含与从集合功能选择到SVM集合的类高度相关的功能，这些功能可以在分类精度的性能上实现。所提出的方法由两个阶段组成：（i）选择通过应用基于合奏的特征选择方法来选择可能是支持向量的特征集； （ii）使用选定的功能构建SVM集合。通过心脏图数据集的实验评估了所提出的方法。使用了四种特征选择技术：（i）基于相关性的，（ii）基于一致性的（iii）Relieff和（iv）信息增益。实验结果表明，与单个SVM分类器和SVM分类器相比，使用信息增益特征选择和基于相关的特征选择相比，使用基于相关的特征选择的分类精度和集合特征选择更高。
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.
