A Novel Classification Method for Class-imbalanced Data and Its Application in microRNA Recognition

International Journal Bioautomation

Yu-Quan Zhu¹  Zhi Yang¹  Xia Geng² 

[1] ;School of Computer Science and Communication Engineering, Jiangsu University, China;

关键词: Non-coding RNA;    Class imbalance;    Ensemble learning;    Adaboost algorithm;   

DOI  :  10.7546/ijba.2018.22.2.133-146

来源: DOAJ

【 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.

【 授权许可】

Unknown