Random and Deterministic Forests
Forest-based classification and prediction is one of the most commonly used nonparametric statistical methods in many scientific and engineering areas, particularly in machine learning and analysis of high-throughput genomic data. In this chapter, we first introduce the construction of random forests and deterministic forests, and then address a fundamental and practical issue on how large the forests need to be.
KeywordsRandom Forest Importance Measure Importance Score Correct Class Importance Index
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- L. Breiman. Bagging predictors. Machine Learning, 26:123–140, 1996.Google Scholar
- T.R. Golub, D.K. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J.P. Mesirov, H. Coller, M.L. Loh, J.R. Downing, M.A. Caligiuri, C.D. Bloomfield, and E.S. Lander. Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science, 286:531–537, 1999.CrossRefGoogle Scholar
- R.J. Klein, C. Zeiss, E.Y. Chew, J.Y. Tsai, R.S. Sackler, C. Haynes, A.K. Henning, J.P. SanGiovanni, S.M. Mane, S.T. Mayne, M.B. Bracken, F.L. Ferris, J. Ott, C. Barnstable, and C. Hoh. Complement factor H polymorphism in age-related macular degeneration. Science, 308:385–389, 2005.CrossRefGoogle Scholar
- Y. Freund and R.E. Schapire. Game theory, on-line prediction and boosting. In In Proceedings of the Ninth Annual Conference on Computational Learning Theory, pages 325–332. ACM Press, 1996.Google Scholar
- R. Genuer, J. M. Poggi, and C. Tuleau. Random forests: some methodological insights. Rapport de Recherche, Institut National de Recherche en Informatique et en Automatique, 2008.Google Scholar