| 한글제목(Korean Title) |
A Feature Selection-based Ensemble Method for Arrhythmia Classification |
| 영문제목(English Title) |
A Feature Selection-based Ensemble Method for Arrhythmia Classification |
| 저자(Author) |
Erdenetuya Namsrai
Tsendsuren Munkhdalai
Meijing Li
Jung-Hoon Shin
Oyun-Erdene Namsrai
Keun Ho Ryu
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| 원문수록처(Citation) |
VOL 09 NO. 01 PP. 0031 ~ 0040 (2013. 03) |
한글내용
(Korean Abstract) |
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영문내용
(English 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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| 키워드(Keyword) |
Data Mining
Ensemble Method
Fe ature Selection
Arrhythmia Classification
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| 파일첨부 |
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