imDC: an ensemble learning method for imbalanced classification with miRNA data.
Imbalances typically exist in bioinformatics and are also common in other areas. A drawback of traditional machine learning methods is the relatively little attention given to small sample classification. Thus, we developed imDC, which uses an ensemble learning concept in combination with weights and sample misclassification information to effectively classify imbalanced data. Our method showed better results when compared to other algorithms with UCI machine learning datasets and microRNA data.
School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China chunyu@hit.edu.cn.
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Wang
School of Information Science and Technology, Xiamen University, Xiamen, China.
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Hu
School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
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Guo
School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
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Liu
School of Information Science and Technology, Xiamen University, Xiamen, China.
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Zou
D000465:Algorithms; D030541:Databases, Genetic; D000069550:Machine Learning; D035683:MicroRNAs / Q000235:genetics* / Q000378:metabolism; D012372:ROC Curve; D015203:Reproducibility of Results
D035683:MicroRNAs