Name
Abstract | ||
|---|---|---|
The any-combiner is a classifier combination approach for target classification problems in which the target class can be naturally decomposed into multiple subclasses. This kind of classification problem can often occur in sensor-based system applications, such as biometric user verification, biosurveillance or underwater mine detection, in which the system goal is to identify a test exemplar as belonging to a category of objects of interest to the exclusion of all other exemplars (clutter). We propose an approach to the target classification problem in which an ensemble of classifier agents are trained to distinguish individual target subclasses from clutter. The any-combiner is then trained by optimizing the multi-agent ensemble for maximum recognition performance across all target subclasses over a range of acceptable operating points. Once deployed, the any-combiner classifies a test example as a target if any of the agents indicates a true positive classification for its target subclass. Experiments show that the any-combiner yields excellent performance on the tasks of biometric verification using face images and underwater object classification using acoustic features. |
Year | Venue | Keywords |
|---|---|---|
2013 | Information Fusion | face recognition,multi-agent systems,object detection,pattern classification,acoustic features,any-combiner,biometric user verification,biometric verification,biosurveillance,classifier agents,classifier combination,clutter,exemplars,face images,maximum recognition performance,multiagent ensemble,multiagent target classification,positive classification,sensor-based system applications,system goal,target classification problems,target subclasses,test exemplar,underwater mine detection,underwater object classification,classification algorithms,face recognition,multi-agent systems |
Field | DocType | ISBN |
One-class classification,User verification,Computer science,Multi-agent system,Artificial intelligence,Classifier (linguistics),Facial recognition system,Object detection,Computer vision,Pattern recognition,Clutter,Biometrics,Machine learning | Conference | 978-605-86311-1-3 |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nathan Parrish | 1 | 0 | 0.34 |
| Ashley J. Llorens | 2 | 4 | 1.84 |