A support vector hierarchical method for multi-class classification and rejection

Yu-Chiang Frank Wang, David P. Casasent

Abstract: We address both recognition of true classes and rejection of unseen false classes inputs, as occurs in many realistic pattern recognition problems. we advance a hierarchical binary-decision classifier and produce analog outputs at each node, with yields a new soft-decision hierarchical is designed by our new support vector clustering method, which selects the classes to be separated at each node in the hierarchy. Use of our SVRDM (support vector representation and discrimination machine) classifiers at each node provides generalization and rejection ability. The soft-decision SVRDM output allows use of the confidence score for each class at each node; this is shown to improve classification (for true classes) and rejection (for false classes) performance. New aspects of this paper are that we provide remarks on our hierarchical design method, including our hierarchical clustering rule, and discuss the meaning and the use of probabilities in our soft-decision hierarchical SVRDM classifiers. We also provide initial tests results on a new database (COIL) that allows large class problem to be addressed. No prior work considered rejection of false classes on this database.