We propose several fusion techniques in the design of a hybrid composite classification system. Our composite
classifier taps into the strengths of two separate classification paradigms and examines various fusion methods
for combining the two. The first classifier uses non-numeric features, similar to those found in syntactic pattern
recognition, by exploiting the overall structure of the patterns themselves. The second method uses a more
classical feature vector method that bins the patterns and uses the maximum values within each bin in developing
the feature vector for each pattern. By using these two separate approaches, we explore conditions that allow
the two techniques to be complementary in nature, thus improving, when fused, the overall performance of the
classification system. We examine four seperate fusion techniques, the Basic Ensemble Method, the Probabilistic
Neural Network, the Borda Count and the Bayesian Belief Network using a ten class problem in our experiments.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
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.