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https://ah.lib.nccu.edu.tw/handle/140.119/66287
題名: | Improving Bagging Performance through Multi-Algorithm Ensembles | 作者: | 徐國偉 Hsu, Kuo-Wei |
貢獻者: | 資科系 | 關鍵詞: | Sensory evoked potential; SEP; Auditory evoked potential; AEP; ERP; MeCP2; Preclinical model; Mouse; Gamma oscillation | 日期: | 2012 | 上傳時間: | 27-May-2014 | 摘要: | Bagging establishes a committee of classifiers first and then aggregates their outcomes through majority voting. Bagging has attracted considerable research interest and been applied in various application domains. Its advantages include an increased capability of handling small data sets, less sensitivity to noise or outliers, and a parallel structure for efficient implementations. However, it has been found to be less accurate than some other ensemble methods. In this paper, we propose an approach that improves bagging through the employment of multiple classification algorithms in ensembles. Our approach preserves the parallel structure of bagging and improves the accuracy of bagging. As a result, it unlocks the power and expands the user base of bagging | 關聯: | Frontiers of Computer Science, 6(5), 498-512 | 資料類型: | article | DOI: | http://dx.doi.org/10.1007/978-3-642-28320-8_40 |
Appears in Collections: | 期刊論文 |
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