CONFERENCE PROCEEDINGS
Advanced Search
Home > Proceedings > Volume 6915 > Article
Translator Disclaimer
Paper
17 March 2008 Hybrid linear classifier for jointly normal data: theory
Weijie Chen, Charles E. Metz, Maryellen L. Giger
Author Affiliations +
Proceedings Volume 6915, Medical Imaging 2008: Computer-Aided Diagnosis; 691504 (2008) https://doi.org/10.1117/12.770326
Event: Medical Imaging, 2008, San Diego, California, United States
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.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Weijie Chen, Charles E. Metz, and Maryellen L. Giger "Hybrid linear classifier for jointly normal data: theory", Proc. SPIE 6915, Medical Imaging 2008: Computer-Aided Diagnosis, 691504 (17 March 2008); https://doi.org/10.1117/12.770326
ACCESS THE FULL ARTICLE
PERSONAL SIGN IN
Full access may be available with your subscription
Forgot your username?
Forgot your password?
No SPIE account? Create an account
Institutional Access:
Sign in with your institutional credentials
PURCHASE THIS CONTENT
SUBSCRIBE TO DIGITAL LIBRARY
50 downloads per 1-year subscription
Members: $195
Non-members: $335 ADD TO CART
25 downloads per 1 - year subscription
Members: $145
Non-members: $250 ADD TO CART
PURCHASE SINGLE ARTICLE
Includes PDF, HTML & Video, when available
Members: $17.00
Non-members: $21.00 ADD TO CART
PROCEEDINGS
 6 PAGES
DOWNLOAD PAPER SAVE TO MY LIBRARY
SHARE
GET CITATION
Lens.org Logo
CITATIONS
Cited by 2 scholarly publications.
Advertisement
Advertisement
KEYWORDS
Computer aided diagnosis and therapy
Medical imaging
Computer aided design
Matrices
Statistical analysis
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top