DMCA
Decision Combination in Multiple Classifier Systems (1994)
| Venue: | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 16. NO. I. JANUARY 1994 |
| Citations: | 377 - 5 self |
BibTeX
@MISC{Ho94decisioncombination,
author = {Tin Kam Ho and Jonathan J. Hull and Sargur N. Srihari},
title = { Decision Combination in Multiple Classifier Systems},
year = {1994}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
A multiple classifier system is a powerful solution to difficult pattern recognition problems involving large class sets and noisy input because it allows simultaneous use of arbitrary feature descriptors and classification procedures. Decisions by the classifiers can be represented as rankings of classes so that they are comparable across different types of classifiers and different instances of a problem. The rankings can be combined by methods that either reduce or rerank a given set of classes. An intersection method and a union method are proposed for class set reduction. Three methods based on the highest rank, the Borda count, and logistic regression are proposed for class set reranking. These methods have been tested in applications on degraded machine-printed characters and words from large lexicons, resulting in substantial improvement in overall correctness.
Keyphrases
multiple classifier system decision combination powerful solution intersection method different instance degraded machine-printed character large lexicon arbitrary feature descriptor simultaneous use different type large class set noisy input difficult pattern recognition problem logistic regression classification procedure substantial improvement union method borda count overall correctness
