The Effect of Data Sampling When Using Random Forest on Imbalanced Bioinformatics Data

Abstract

Ensemble learning is a powerful tool that has shown promise when applied towards bioinformatics datasets. In particular, the Random Forest classifier has been an effective and popular algorithm due to its relatively good classification performance and its ease of use. However, Random Forest does not account for class imbalance which is known for decreasing… (More)
DOI: 10.1109/IRI.2015.76

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@article{Dittman2015TheEO, title={The Effect of Data Sampling When Using Random Forest on Imbalanced Bioinformatics Data}, author={David J. Dittman and Taghi M. Khoshgoftaar and Amri Napolitano}, journal={2015 IEEE International Conference on Information Reuse and Integration}, year={2015}, pages={457-463} }