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Random Forest with 200 Selected Features: An Optimal Model for Bioinformatics Research.

, , , and . ICMLA (1), page 154-160. IEEE, (2013)

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A New Fixed-Overlap Partitioning Algorithm for Determining Stability of Bioinformatics Gene Rankers., , and . ICMLA (2), page 170-177. IEEE, (2012)978-1-4673-4651-1.Feature list aggregation approaches for ensemble gene selection on patient response datasets., , , and . IRI, page 317-324. IEEE Computer Society, (2013)Stability Analysis of Feature Ranking Techniques on Biological Datasets., , , and . BIBM, page 252-256. IEEE Computer Society, (2011)A review of the stability of feature selection techniques for bioinformatics data., , , , and . IRI, page 356-363. IEEE, (2012)Selecting the Appropriate Ensemble Learning Approach for Balanced Bioinformatics Data., , and . FLAIRS, page 329-334. AAAI Press, (2015)Using Random Undersampling to Alleviate Class Imbalance on Tweet Sentiment Data., , , and . IRI, page 197-202. IEEE Computer Society, (2015)Random Forest with 200 Selected Features: An Optimal Model for Bioinformatics Research., , , and . ICMLA (1), page 154-160. IEEE, (2013)Simplifying the Utilization of Machine Learning Techniques for Bioinformatics., , , and . ICMLA (2), page 396-403. IEEE, (2013)978-0-7695-5144-9.Determining the Number of Iterations Appropriate for Ensemble Gene Selection on Microarray Data., , , and . ICMLA (1), page 82-89. IEEE, (2012)Selecting the Appropriate Data Sampling Approach for Imbalanced and High-Dimensional Bioinformatics Datasets., , and . BIBE, page 304-310. IEEE Computer Society, (2014)