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Classifier designs for binary classifications of ground vehicles

Our goal for this study is to construct classifiers with minimum classification error rates for three binary classification problems based on their acoustic emissions, namely tracked versus wheeled vehicles, heavy-tracked versus light-tracked vehicles, and heavy-wheeled versus light-wheeled vehicles. Because the acoustic measurements of a run correspond to tens or hundreds of seconds, and are time-varying, we segment them into one-second data blocks, and use the data blocks (which we call prototypes) for classification. The magnitudes of the second through 12th harmonics of each prototype are used as the features. We find, by analyzing the features within each run and across runs, that the run-means and run-standard-deviations of the features vary from run to run for all kinds of vehicles. We therefore use type-2 fuzzy sets to model the uncertainties contained in these features, and then construct type-2 fuzzy logic rule-based classifiers (FL-RBC) for these three binary classification problems. To evaluate the performance of the type-2 FL-RBCs in a fair way, we also construct the Bayesian classifiers and type-1 FL-RBCs, and compare their performance through leave-one-out experiments. Our experiments show that both the type-1 and type-2 FL-RBCs have significantly better performance than the Bayesian classifier, and the type-2 FL-RBC has better performance than the type-1 FL-RBC for all three classification problems. So we conclude that the type-2 FL-RBCs are the desired classifiers for these three binary classification problems.