imDC: An ensemble learning method for imbalanced classification with miRNA data

Genetics and Molecular Research

Volumn 14, Issue 1, 2015, Pages 123-133

  Hu, L L ^b   Zou, Q ^b   Liu, X Y ^a  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

^b XIAMEN UNIVERSITY   (China)

Author keywords

Bioinformatics; Ensemble learning; Imbalances; Machine learning; miRNA

Indexed keywords

MESSENGER RNA; MICRORNA;

ALGORITHM; ARTICLE; BIOINFORMATICS; CALCULATION; DATA BASE; GENE EXPRESSION; GENE SEQUENCE; INFORMATION PROCESSING; INFORMATION RETRIEVAL; MACHINE LEARNING; GENETIC DATABASE; GENETICS; METABOLISM; RECEIVER OPERATING CHARACTERISTIC; REPRODUCIBILITY;

ALGORITHMS; DATABASES, GENETIC; MACHINE LEARNING; MICRORNAS; REPRODUCIBILITY OF RESULTS; ROC CURVE;

EID: 84921263022     PISSN: None     EISSN: 16765680     Source Type: Journal    
DOI: 10.4238/2015.January.15.15     Document Type: Article

References (14)

1. Chawla NV, Bowyer KW, Hall LO and Kegelmeyer WP. (2002). SMOTE: Synthetic Minority Over-Sampling Technique. Journal of Artificial Intelligence Research. 16:321-357
2. Diettrich TG (2000). Ensemble methods in machine learning. MCS’00 Proceedings of the First International Worshop on Multiple Classifier Systems. Cagliari, Italy.
3. Dietterich TG (2002). Ensemble learning. In: The Handbook of Brain Theory and Neural Networks, 2nd edn. The MIT Press, Cambridge.
4. Drown DJ, Khoshgoftaar TM and Narayanan R (2007). Using evolutionary sampling to mine imbalanced data. The 6th International Conference on Machine Learning and Applications. Washington DC: IEEE Computer Society 363-368.
5. Krogh A, et al. (1995). Neural network ensembles, cross validation, and active learning. In: Advances in neural information processing systems. MIT Press, Cambridge, 231-238.
6. Kubat M, Holte RC and Matwin S (1998). Machine learning for the detection of oil spills in satellite radar images. Machine Learning 30: 195-215.
7. Lee Y, Lin Y and Wahba G (2004). Multicategory support vector machines: theory and application to the classification of microarray data and satellite radiance data. Journal of the American Statistical Association 99: 67-81.
8. Lin C, Zou Y, Qin J, Liu X, et al. (2013). Hierarchical classification of protein folds using a novel ensemble classifier. PLoS One 8: e56499.
9. Lin C, Chen W, Qiu C, Wu Y, et al. (2014). LibD3C: Ensemble classifiers with a clustering and dynamic selection strategy. Neurocomputing 123: 424-435.
10. Liu XY, Wu JX and Zhou ZH (2006). A cascade-based classification method for class-imbalanced data. J. Nanjing Univ. (Natural Sci.). 42: 148-155.
11. Schapire RE (1990). The strength of weak learnability. Machine Learning 5: 197-227.
12. Song L, Li D, Zeng X, Wu Y, et al. (2014). nDNA-prot: identification of DNA-binding proteins based on unbalanced classification. BMC Bioinformatics 15: 298.
13. Quan Z, Maozu G, Yang L and Jun W (2010). A classification method for class-imbalanced data and its application on bioinformatics. J. Computer Research and Development 47:1407-1414.
14. Xue CG, Fei L, Tao H, Liu GP, et al. (2005). Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine. BMC Bioinformatics, 6: 310