C4.5: programs for machine learning
C4.5: programs for machine learning
Boosting a weak learning algorithm by majority
Information and Computation
Machine Learning
Decision forests with oblique decision trees
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
MML inference of oblique decision trees
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Multi-Test decision trees for gene expression data analysis
SIIS'11 Proceedings of the 2011 international conference on Security and Intelligent Information Systems
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Building classification models plays an important role in DNA mircroarray data analyses. An essential feature of DNA microarray data sets is that the number of input variables (genes) is far greater than the number of samples. As such, most classification schemes employ variable selection or feature selection methods to pre-process DNA microarray data. This paper investigates various aspects of building classification models from microarray data with tree-based classification algorithms by using Partial Least-Squares (PLS) regression as a feature selection method. Experimental results show that the Partial Least-Squares (PLS) regression method is an appropriate feature selection method and tree-based ensemble models are capable of delivering high performance classification models for microarray data.