Quantifying the expertise of classifiers using 4-value logic

Amy L. Magnus; Mark E. Oxley

doi:10.1117/12.458705

11 March 2002 Quantifying the expertise of classifiers using 4-value logic

Amy L. Magnus, Mark E. Oxley

Proceedings Volume 4739, Applications and Science of Computational Intelligence V; (2002) https://doi.org/10.1117/12.458705
Event: AeroSense 2002, 2002, Orlando, FL, United States

Abstract

An intelligent agent---defined as an autonomous, adaptive, cooperative computer program---must credibly represent its expertise in negotiations with peer agents. Given an agent-based classifier, the determination of where in the domain the classifier is an expert must be explicitly stated. Likewise, where the classifier is confused should also be represented. Currently, an error measures provides an estimate of the relative size of the expertise and confusion sets, but error does not offer a distinct opinion on an untruthed feature vector's membership---i.e., whether its classification is based on specific information, conjecture or chance. We propose the theory for estimating the complete membership of a classifier's expertise sets and confusion sets. From these sets, we construct a 4-value classifier that hypothesizes for each new feature vector whether its classification can be made confidently or not. Examples are given that demonstrate the utility of this theory using multilayer perceptrons.

Citation Download Citation

Amy L. Magnus and Mark E. Oxley "Quantifying the expertise of classifiers using 4-value logic", Proc. SPIE 4739, Applications and Science of Computational Intelligence V, (11 March 2002); https://doi.org/10.1117/12.458705

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

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