Another strategy is to look at subsets of the problem. For example, if
you drop class d, are a, b, and c still distinguished? What if you just
give the classifier classes a and b?
Jo
On 8/13/2014 5:45 PM, Hanson, Gavin Keith wrote:
Hi all,
I’m just wondering if anyone has any advice on some ways to deal with
evaluating classifier performance on a 4-way problem.
I’ve been using the BayesConfusionHypothesis tool which works quite
well, but I just wondered what else was out there by way of quantitative
evidence to insure that classifier accuracy isn’t being driven by
perfect classification between 2 labels, and confusion between the other
2, or whatever. Just glancing at the confusion matrices can give us a
good idea about what ROIs are confusing certain conditions, but a more
objective solution would be nice.
The problem seems to be kinda sidestepped in some of the literature on
pattern classification in MRI.
- Gavin
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Gavin Hanson, B.S.
Research Assistant
Department of Psychology
University of Kansas
1415 Jayhawk Blvd., 534 Fraser Hall
Lawrence, KS 66045
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Joset A. Etzel, Ph.D.
Research Analyst
Cognitive Control & Psychopathology Lab
Washington University in St. Louis
http://mvpa.blogspot.com/
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