Building Classification Models from Microarray Data with Tree-Based Classification Algorithms

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.

• Type: Conference
• Year: 2007
• Where: AUSAI
• Authors: Peter J. Tan, David L. Dowe, Trevor I. Dix
• Keywords: Artificial Intelligence, AUSAI 2007, DNA Microarray Data, Feature Selection Method, Microarray Data