Accession Number : ADA338974
Title : Voting Techniques for Combining Multiple Classifiers.
Descriptive Note : Final rept. Apr-Aug 97,
Corporate Author : ARMY RESEARCH LAB ADELPHI MD
Personal Author(s) : Thomas, Diana ; Der, Sandor Z.
PDF Url : ADA338974
Report Date : MAR 1998
Pagination or Media Count : 22
Abstract : Algorithms for decision fusion are surveyed and qualitatively compared for the problem of classification of targets. The methods assume that a number of imperfect classifiers are available, and that these classifiers have enough statistical independence that significant improvement can be made by good combination algorithms. Optimal solutions for this problem require an exact statistical model of the classifiers and the decision space, which are rarely available for real-world problems. Consequently, algorithms must be chosen by intuition and then tested empirically for comparison. Through qualitative comparison, one can reduce the number of algorithms that need to be implemented by eliminating those algorithms that are likely to be weak combiners or to show poor generalization capability. This report surveys candidate algorithms that are likely to show good generalization performance for later empirical evaluation.
Descriptors : *TARGET RECOGNITION, *IDENTIFICATION SYSTEMS, *STATISTICAL ANALYSIS, MATHEMATICAL MODELS, ALGORITHMS, OPTIMIZATION, COMPARISON, TEST EQUIPMENT, SOLUTIONS(GENERAL), SURVEYS, EXPERT SYSTEMS, TARGET CLASSIFICATION, LOW STRENGTH, DECODING.
Subject Categories : Statistics and Probability
Target Direction, Range and Position Finding
Distribution Statement : APPROVED FOR PUBLIC RELEASE