JSM 2005 Online Program

Abstract #303442

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Activity Number:	226
Type:	Contributed
Date/Time:	Tuesday, August 9, 2005 : 8:30 AM to 10:20 AM
Sponsor:	Biometrics Section
Abstract - #303442
Title:	CLASSIX: A New Classification Method Based on a Separation Index with Applications in Genomics
Author(s):	Weiliang Qiu*+ and Mei-Ling T. Lee
Companies:	Harvard Medical School and Harvard University
Address:	181 Longwood Avenue, Boston, MA, 02115, United States
Keywords:	Microarray Data ; Classification Methods ; Linear Discriminant Analysis ; Simulation
Abstract:	Linear discriminant analysis (LDA) is one of the commonly used classification methods. It is simple to implement and easy to use. The LDA method is based on an assumption that the covariance matrices of the two classes are the same. In practice, however, data in two classes often have different covariance matrices. In these cases, the performance of LDA may not be satisfactory. To remedy this situation, we propose a new CLASsification method based on a Separation IndeX (CLASSIX for short) to relax the equal-covariance-matrix assumption required by LDA. The decision boundary of CLASSIX is linear. Using a well-known microarray dataset on leukemia study and simulated datasets, we show the proposed CLASSIX method performs better than LDA.

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