A Novel Classification Method for Class-imbalanced Data and Its Application in microRNA Recognition
| Autor: | Xia Geng, Yu-Quan Zhu, Zhi Yang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: | |
| Zdroj: | International Journal Bioautomation, Vol 22, Iss 2, Pp 133-146 (2018) |
| Druh dokumentu: | article |
| ISSN: | 1314-1902 1314-2321 |
| DOI: | 10.7546/ijba.2018.22.2.133-146 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
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