Adaptive Hierarchial Classification with Limited Training Data

This research focused on the development of a approach for classification that is robust with respect to training data that are limited both in quantity and spatial extent. Many difficult classification problems involve a high dimensional input and output space (candidate labels). Due to the curse of dimensionality, it is necessary to reduce the size of the input space when there is only a limited quantity of training data available. While a significant amount of research has focused on transforming the input space into a reduced feature space that accurately discriminates between the classes in a fixed output space, traditional approaches fail to capitalize on the domain knowledge and flexibility gained by transforming the feature space and the output space simultaneously. A new approach is proposed that utilizes domain knowledge, which is automatically discovered from the data, to combat the smail sample size problem.