  Microarray is a technology to quantitatively monitor the expression of large
number of genes in parallel. It has become one of the main tools for global
gene expression analysis in molecular biology research in recent years. The
large amount of expression data generated by this technology makes the study of
certain complex biological problems possible and machine learning methods are
playing a crucial role in the analysis process. At present, many machine
learning methods have been or have the potential to be applied to major areas
of gene expression analysis. These areas include clustering, classification,
dynamic modeling and reverse engineering.
  In this thesis, we focus our work on using machine learning methods to solve
the classification problems arising from microarray data. We first identify the
major types of the classification problems; then apply several machine learning
methods to solve the problems and perform systematic tests on real and
artificial datasets. We propose improvement to existing methods. Specifically,
we develop a multivariate and a hybrid feature selection method to obtain high
classification performance for high dimension classification problems. Using
the hybrid feature selection method, we are able to identify small sets of
features that give predictive accuracy that is as good as that from other
methods which require many more features.
