I hereby declare that I am the sole author of this thesis. This is a true copy of the thesis, including any required final revisions, as accepted by my examiners. I understand that my thesis may be made electronically available to the public. ii The work reported in the thesis consists of two parts. One part is concerned with the development of a feature selection method based on statistical significance test, which can be generally used in any supervised pattern classification. The other part applies this proposed feature selection method to conduct proteomic pattern analysis for prostate cancer detection. For a given classification problem, we need to determine a set of relevant features to generate a classifier. In real-world problems, many features in initial feature set are usually irrelevant to the classification task and redundant with each other, which will increase the computational complexity and reduce the recognition rate. The task of feature selection is to choose a small feature subset in order to achieve better classification performance. As such,

Abstract not found