Share Email Print

Proceedings Paper

Hybrid linear classifier for jointly normal data: theory
Author(s): Weijie Chen; Charles E. Metz; Maryellen L. Giger
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Classifier design for a given classification task needs to take into consideration both the complexity of the classifier and the size of the data set that is available for training the classifier. With limited training data, as often is the situation in computer-aided diagnosis of medical images, a classifier with simple structure (e.g., a linear classifier) is more robust and therefore preferred. We consider the two-class classification problem in which the feature data arise from two multivariate normal distributions. A linear function is used to combine the multi-dimensional feature vector onto a scalar variable. This scalar variable, however, is generally not an ideal decision variable unless the covariance matrices of the two classes are equal. We propose using the likelihood ratio of this scalar variable as a decision variable and, thus, generalizing the traditional classification paradigm to a hybrid two-stage procedure: a linear combination of the feature vector elements to form a scalar variable followed by a nonlinear, nonmonotic transformation that maps the scalar variable onto its likelihood ratio (i.e., the ideal decision variable, given the scalar variable). We show that the traditional Fisher's linear discriminant function is generally not the optimal linear function for the first stage in this two-stage paradigm. We further show that the optimal linear function can be obtained with a numerical optimization procedure using the area under the "proper" ROC curve as the objective function.

Paper Details

Date Published: 17 March 2008
PDF: 6 pages
Proc. SPIE 6915, Medical Imaging 2008: Computer-Aided Diagnosis, 691504 (17 March 2008); doi: 10.1117/12.770326
Show Author Affiliations
Weijie Chen, U.S. Food and Drug Administration (United States)
Charles E. Metz, The Univ. of Chicago (United States)
Maryellen L. Giger, The Univ. of Chicago (United States)

Published in SPIE Proceedings Vol. 6915:
Medical Imaging 2008: Computer-Aided Diagnosis
Maryellen L. Giger; Nico Karssemeijer, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?