Relationship between classifier performance and distributional complexity for small samples

• Main Author: Attoor, Sanju Nair
• Other Authors: Dougherty, Edward R.
• Format: Others
• Language: en_US
• Published: Texas A&M University 2004
• Subjects: classifier; small sample; distributional complexity; VC dimension
• Online Access: http://hdl.handle.net/1969.1/1201

Given a limited number of samples for classification, several issues arise with respect to design, performance and analysis of classifiers. This is especially so in the case of microarray-based classification. In this paper, we use a complexity measure based mixture model to study classifier performance for small sample problems. The motivation behind such a study is to determine the conditions under which a certain class of classifiers is suitable for classification, subject to the constraint of a limited number of samples being available. Classifier study in terms of the VC dimension of a learning machine is also discussed.