Machine Learning in Astronomy: A Case Study in Quasar-Star Classification
This work addresses classification challenges in astronomy, but it is incremental as it builds on existing methods.
The paper tackled the problem of classifying stars versus quasars using SDSS data, finding that asymmetric AdaBoost is effective for photometric classification.
We present the results of various automated classification methods, based on machine learning (ML), of objects from data releases 6 and 7 (DR6 and DR7) of the Sloan Digital Sky Survey (SDSS), primarily distinguishing stars from quasars. We provide a careful scrutiny of approaches available in the literature and have highlighted the pitfalls in those approaches based on the nature of data used for the study. The aim is to investigate the appropriateness of the application of certain ML methods. The manuscript argues convincingly in favor of the efficacy of asymmetric AdaBoost to classify photometric data. The paper presents a critical review of existing study and puts forward an application of asymmetric AdaBoost, as an offspring of that exercise.