We present a new classification method for quasar identification in the EROS-2 and MACHO data sets based on a boosted version of a random forest classifier. We use a set of variability features including parameters of a continuous autoregressive model. We prove that continuous autoregressive parameters are very important discriminators in the classification process. We create two training sets (one for EROS-2 and one for MACHO data sets) using known quasars found in the Large Magellanic Cloud (LMC). Our model's accuracy in both EROS-2 and MACHO training sets is about 90 per cent precision and 86 per cent recall, improving the state-of-the-art models, accuracy in quasar detection. We apply the model on the complete, including 28 million objects, EROS-2 and MACHO LMC data sets, finding 1160 and 2551 candidates, respectively. To further validate our list of candidates, we cross-matched our list with 663 previously known strong candidates, getting 74 per cent of matches for MACHO and 40 per cent in EROS.
The main difference on matching level is because EROS-2 is a slightly shallower survey which translates to significantly lower signal-to-noise ratio light curves.