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Using Random Undersampling to Alleviate Class Imbalance on Tweet Sentiment Data.

, , , and . IRI, page 197-202. IEEE Computer Society, (2015)

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Using Correlation-Based Feature Selection for a Diverse Collection of Bioinformatics Datasets., , and . BIBE, page 156-162. IEEE Computer Society, (2014)Selecting the Appropriate Data Sampling Approach for Imbalanced and High-Dimensional Bioinformatics Datasets., , and . BIBE, page 304-310. IEEE Computer Society, (2014)Network Traffic Prediction Models for Near- and Long-Term Predictions., , , and . BIBE, page 362-368. IEEE Computer Society, (2014)Experimental perspectives on learning from imbalanced data., , and . ICML, volume 227 of ACM International Conference Proceeding Series, page 935-942. ACM, (2007)Simplifying the Utilization of Machine Learning Techniques for Bioinformatics., , , and . ICMLA (2), page 396-403. IEEE, (2013)978-0-7695-5144-9.Random Forest with 200 Selected Features: An Optimal Model for Bioinformatics Research., , , and . ICMLA (1), page 154-160. IEEE, (2013)Comparative Analysis on the Stability of Feature Selection Techniques Using Three Frameworks on Biological Datasets., , , and . ICMLA (1), page 418-423. IEEE, (2013)Studying the Effect of Class Imbalance in Ocean Turbine Fault Data on Reliable State Detection., , and . ICMLA (1), page 268-275. IEEE, (2012)Determining the Number of Iterations Appropriate for Ensemble Gene Selection on Microarray Data., , , and . ICMLA (1), page 82-89. IEEE, (2012)Using Twitter Content to Predict Psychopathy., , , and . ICMLA (2), page 394-401. IEEE, (2012)978-1-4673-4651-1.