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Decision forest for classification of gene expression data

Authors
Journal
Computers in Biology and Medicine
0010-4825
Publisher
Elsevier
Publication Date
Volume
40
Issue
8
Identifiers
DOI: 10.1016/j.compbiomed.2010.06.004
Keywords
  • Decision Forest
  • Gene Expression Data
  • Classification
  • Microarray
  • Ensemble
Disciplines
  • Computer Science

Abstract

Abstract This study attempts to propose an improved decision forest (IDF) with an integrated graphical user interface. Based on four gene expression data sets, the IDF not only outperforms the original decision forest, but also is superior or comparable to other state-of-the-art machine learning methods, especially in dealing with high dimensional data. With an integrated built-in feature selection (FS) mechanism and fewer parameters to tune, it can be trained more efficiently than methods such as support vector machine, and can be built with much fewer trees than other popular tree-based ensemble methods. Moreover, it suffers less from the curse of dimensionality.

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