A novel classification method for class-imbalanced data and its application in microRNA recognition

被引：1
作者：
Geng X. [1 ]
Zhu Y.-Q. [1 ]
Yang Z. [2 ]
机构：
[1] School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, Jiangsu
[2] School of Management, Jiangsu University, Zhenjiang, Jiangsu
来源：
International Journal Bioautomation | 2018年 / 22卷 / 02期
关键词：
Adaboost algorithm; Class imbalance; Ensemble learning; Non-coding RNA;
D O I：
10.7546/ijba.2018.22.2.133-146
中图分类号：
学科分类号：
摘要：
For non-coding RNA gene mining, especially microRNA mining, there are many challenges in the classification of imbalanced data. A novel classification method based on the Adaboost algorithm is proposed to handle the imbalance of positive and negative cases. Unstable-Adaboost is improved with respect to the initial weight assignment, the base classifier selection, the weight adjustment mechanism and other aspects. Furthermore, the Stable-Adaboost algorithm is proposed, which adjusts the weight of the sample set to rapidly achieve a more balanced training set. In addition, the Stable-Adaboost algorithm can ensure that the follow-up training set is maintained in a balanced state by optimizing the weight adjustment mechanism of incorrectly classified samples and stabilizing the classification performance. Experimental results show the superiority of Unstable-Adaboost and Stable- Adaboost in imbalance classification. © 2018 by the authors.
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页码：133 / 146
页数：13
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