@article{parrish2020an,
author = {Parrish, Nathan H. and Llorens, Ashley J. and Driskell, Alex E.},
title = {An Agent-Ensemble for Thresholded Multi-Target Classification},
year = {2020},
month = {February},
abstract = {We propose an ensemble approach for multi-target binary classification, where the target class breaks down into a disparate set of pre-defined target-types. The system goal is to maximize the probability of alerting on targets from any type while excluding background clutter. The agent-classifiers that make up the ensemble are binary classifiers trained to classify between one of the target-types vs. clutter. The agent ensemble approach offers several benefits for multi-target classification including straightforward in-situ tuning of the ensemble to drift in the target population and the ability to give an indication to a human operator of which target-type causes an alert. We propose a combination strategy that sums weighted likelihood ratios of the individual agent-classifiers, where the likelihood ratio is between the target-type for the agent vs. clutter. We show that this combination strategy is optimal under a conditionally non-discriminative assumption. We compare this combiner to the common strategy of selecting the maximum of the normalized agent-scores as the combiner score. We show experimentally that the proposed combiner gives excellent performance on the multi-target binary classification problems of pin-less verification of human faces and vehicle classification using acoustic signatures.},
url = {http://approjects.co.za/?big=en-us/research/publication/an-agent-ensemble-for-thresholded-multi-target-classification/},
pages = {1376},
journal = {Applied Sciences},
volume = {10},
number = {4},
}