An Efficient Method for Rare Spectra Retrieval in Astronomical Databases

Abstract

One of the most important aims of astronomical data mining is to systematically search for specific rare objects in a massive spectral data set, given a small fraction of identified samples with the same type. Most existing methods are mainly based on binary classification, which usually suffers from incompleteness when there are too few known samples. Rank-based methods could provide good solutions for such cases. After investigating several algorithms, a method combining a bipartite ranking model with bootstrap aggregating techniques was developed in this paper. The method was applied while searching for carbon stars in the spectral data of Sloan Digital Sky Survey Data Release 10 and compared with several other popular methods used for data mining. Experimental results validate that the proposed method is not only the most effective but also the least time-consuming technique among its competitors when searching for rare spectra in a large but unlabeled data set.