Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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A Feature Selection-based Ensemble Method for Arrhythmia Classification

  • Namsrai, Erdenetuya (Database/Bioinformatics Laboratory, Chungbuk National University) ;
  • Munkhdalai, Tsendsuren (Database/Bioinformatics Laboratory, Chungbuk National University) ;
  • Li, Meijing (Database/Bioinformatics Laboratory, Chungbuk National University) ;
  • Shin, Jung-Hoon (Dept. of Software Engineering, Chonbuk National University) ;
  • Namsrai, Oyun-Erdene (Dept. of Information Technology, Mongolian National University) ;
  • Ryu, Keun Ho (Database/Bioinformatics Laboratory, Chungbuk National University)
  • Received : 2012.02.17
  • Accepted : 2012.11.22
  • Published : 2013.03.31
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Abstract

In this paper, a novel method is proposed to build an ensemble of classifiers by using a feature selection schema. The feature selection schema identifies the best feature sets that affect the arrhythmia classification. Firstly, a number of feature subsets are extracted by applying the feature selection schema to the original dataset. Then classification models are built by using the each feature subset. Finally, we combine the classification models by adopting a voting approach to form a classification ensemble. The voting approach in our method involves both classification error rate and feature selection rate to calculate the score of the each classifier in the ensemble. In our method, the feature selection rate depends on the extracting order of the feature subsets. In the experiment, we applied our method to arrhythmia dataset and generated three top disjointed feature sets. We then built three classifiers based on the top-three feature subsets and formed the classifier ensemble by using the voting approach. Our method can improve the classification accuracy in high dimensional dataset. The performance of each classifier and the performance of their ensemble were higher than the performance of the classifier that was based on whole feature space of the dataset. The classification performance was improved and a more stable classification model could be constructed with the proposed approach.

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