A Supra-Classifier Architecture for Scalable Knowledge Reuse

Abstract

When faced with inadequate information, humans often use knowledge gained from previous experience to help them in making decisions. Even when this knowledge is spread thinly among many previous experiences, humans are able to effectively accumulate and apply it to a current classification task of interest. Inspired by human knowledge reuse, we have previously introduced a general framework for the use of knowledge embodied in existing classifiers to aid in a new classification task. In this framework, a supraclassifier is built to make decisions based on the outputs of large numbers of previously trained classifiers designed for different, but possibly relevant tasks. In this article, we discuss the Hamming Nearest Neighbor (HNN) supraclassifier architecture and mathematically show its usefulness. Experiments on public domain data sets demonstrate the practicality of the framework and HNN supraclassifier when faced with very few training examples. Keywor...