Sample Subset Optimization Techniques for Imbalanced and Ensemble Learning Problems in Bioinformatics Applications

Data sampling is a widely used technique in a broad range of machine learning problems. Traditional sampling approaches generally rely on random resampling from a given dataset. However, these approaches do not take into consideration additional information, such as sample quality and usefulness. We...

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Bibliographic Details
Published in:IEEE transactions on cybernetics 2014-03, Vol.44 (3), p.445-455
Main Authors: Pengyi Yang, Yoo, Paul D., Fernando, Juanita, Zhou, Bing B., Zili Zhang, Zomaya, Albert Y.
Format: Article
Language:English
Subjects:
Algorithms
Bioinformatics
Bioinformatics applications
Computational Biology - methods
Computer Simulation
Cybernetics
Data Interpretation, Statistical
Data sampling
ensemble learning
imbalanced learning
Learning
Machine Learning
Models, Statistical
Optimization
Pattern Recognition, Automated - methods
Protein engineering
Proteins
Sample Size
sample subset optimization (SSO)
Sampling
Sampling techniques
Sociology
Statistics
Training
under-sampling
Utilities
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Summary:Data sampling is a widely used technique in a broad range of machine learning problems. Traditional sampling approaches generally rely on random resampling from a given dataset. However, these approaches do not take into consideration additional information, such as sample quality and usefulness. We recently proposed a data sampling technique, called sample subset optimization (SSO). The SSO technique relies on a cross-validation procedure for identifying and selecting the most useful samples as subsets. In this paper, we describe the application of SSO techniques to imbalanced and ensemble learning problems, respectively. For imbalanced learning, the SSO technique is employed as an under-sampling technique for identifying a subset of highly discriminative samples in the majority class. In ensemble learning, the SSO technique is utilized as a generic ensemble technique where multiple optimized subsets of samples from each class are selected for building an ensemble classifier. We demonstrate the utilities and advantages of the proposed techniques on a variety of bioinformatics applications where class imbalance, small sample size, and noisy data are prevalent.
ISSN:2168-2267
2168-2275
DOI:10.1109/TCYB.2013.2257480