An Agent-Ensemble for Thresholded Multi-Target Classification

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.