Classification by Boosting Differences in Input Vectors: An application to datasets from Astronomy

There are many occasions when one does not have complete information in order to classify objects into different classes, and yet it is important to do the best one can since other decisions depend on that. In astronomy, especially time-domain astronomy, this situation is common when a transient is detected and one wishes to determine what it is in order to decide if one must follow it. We propose to use the Difference Boosting Neural Network (DBNN) which can boost differences between feature vectors of different objects in order to differentiate between them. We apply it to the publicly available data of the Catalina Real-Time Transient Survey (CRTS) and present preliminary results. We also describe another use with a stellar spectral library to identify spectra based on a few features. The technique itself is more general and can be applied to a varied class of problems.