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

C. Y. Wang; M. Z. Guo; X. Y. Liu

doi:10.4238/2015.January.15.15

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

C. Y. Wang^*
, M. Z. Guo
, X. Y. Liu

^*Corresponding author for this work

School of Computer Science and Technology, Harbin Institute of Technology
Xiamen University

Research output: Contribution to journal › Article › peer-review

39 Scopus citations

Abstract

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.

Original language	English
Pages (from-to)	123-133
Number of pages	11
Journal	Genetics and Molecular Research
Volume	14
Issue number	1
DOIs	https://doi.org/10.4238/2015.January.15.15
State	Published - 15 Jan 2015
Externally published	Yes

Keywords

Bioinformatics
Ensemble learning
Imbalances
Machine learning
miRNA

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AB - 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.

KW - Bioinformatics

KW - Ensemble learning

KW - Imbalances

KW - Machine learning

KW - miRNA

UR - https://www.scopus.com/pages/publications/84921263022

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

Abstract

Keywords

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