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Rotation of Random Forests for Genomic and Proteomic Classification Problems

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Software Tools and Algorithms for Biological Systems

Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 696))

Abstract

Random Forests have been recently widely used for different kinds of classification problems. One of them is classification of gene expression samples that is known as a problem with extremely high dimensionality, and therefore demands suited classification techniques. Due to its strong robustness with respect to large feature sets, Random Forests show significant increase of accuracy in comparison to other ensemble-based classifiers that were widely used before its introduction. In this chapter, we present another ensemble of decision trees called Rotation Forest and evaluate its classification performance on different microarray datasets. Rotation Forest can also be applied to different already existing ensembles of classifiers like Random Forest to improve their accuracy and robustness. This study presents evaluation of Rotation Forest classification technique based on decision trees as base classifiers and was evaluated on 14 different datasets with genomic and proteomic data. It is evident that Rotation Forest as well as the proposed rotation of Random Forests outperform most widely used ensembles of classifiers including Random Forests on majority of datasets.

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Author information

Authors and Affiliations

  1. Faculty of Health Sciences, University of Maribor, Zitna ulica 15, 2000, Maribor, Slovenia

    Gregor Stiglic

  2. Faculty of Electrical Engineering and Computer Science, University of Maribor, Smetanova 17, 2000, Maribor, Slovenia

    Gregor Stiglic

Authors
  1. Gregor Stiglic
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  2. Juan J. Rodriguez
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  3. Peter Kokol
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Corresponding author

Correspondence to Gregor Stiglic .

Editor information

Editors and Affiliations

  1. Dept. Computer Science, University of Georgia, Athens, 30602-7404, Georgia, USA

    Hamid R. Arabnia

  2. , Department of Computer Science, Lamar University, Beaumont, 77710, Texas, USA

    Quoc-Nam Tran

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Stiglic, G., Rodriguez, J.J., Kokol, P. (2011). Rotation of Random Forests for Genomic and Proteomic Classification Problems. In: Arabnia, H., Tran, QN. (eds) Software Tools and Algorithms for Biological Systems. Advances in Experimental Medicine and Biology, vol 696. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-7046-6_21

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