A study of supervised classification of Hipparcos variable stars using PCA and Support Vector Machines

Abstract

We report on the automated classification of Hipparcos variable stars by a supervised classification algorithm known as Support Vector Machines. The dataset comprised about 3200 stars, each characterized by 51 features. These are the B-V and V-I colours, the skewness of the lightcurve, the median subtracted 10-percentiles and forty bins from the Fourier envelope of the lightcurve. We also tested whether the classification performance can be improved by using the most significant principal components calculated from this dataset. We show that the overall classification performance (as measured by the fraction of true positives) on the original dataset is of the order of 62%. For about 9 of the 18 different variability classes, the classification accuracy is significantly larger than 60% (up to 98%). Introducing principal components does not significantly improve this result. We further find that many of the different variability classes are not very distinct and possibly poorly defined, i.e. there exists a considerable class overlap. It is concluded that this `contamination' of the template set implies minimum errors and thus degrades the overall performance.