Classification of Battlefield Ground Vehicles Using Acoustic Features and Fuzzy Logic Rule-Based Classifiers
Hongwei Wu
Jerry M. Mendel
Dept. of Biochem. & Molecular Biol., Georgia Univ., Athens, GA;
This paper appears in: Fuzzy Systems, IEEE Transactions on
Publication Date: Feb. 2007
Volume: 15,
Issue: 1
On page(s): 56-72
ISSN: 1063-6706
INSPEC Accession Number: 9319013
Digital Object Identifier: 10.1109/TFUZZ.2006.889760
Current Version Published: 2007-02-12
Abstract
In this paper, we demonstrate, through the multicategory classification of battlefield ground vehicles using acoustic features, how it is straightforward to directly exploit the information inherent in a problem to determine the number of rules, and subsequently the architecture, of fuzzy logic rule-based classifiers (FLRBC). We propose three FLRBC architectures, one non-hierarchical and two hierarchical (HFLRBC), conduct experiments to evaluate the performances of these architectures, and compare them to a Bayesian classifier. Our experimental results show that: 1) for each classifier the performance in the adaptive mode that uses simple majority voting is much better than in the non-adaptive mode; 2) all FLRBCs perform substantially better than the Bayesian classifier; 3) interval type-2 (T2) FLRBCs perform better than their competing type-1 (T1) FLRBCs, although sometimes not by much; 4) the interval T2 nonhierarchical and HFLRBC-series architectures perform the best; and 5) all FLRBCs achieve higher than the acceptable 80% classification accuracy
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