Protostellar classification using supervised machine learning algorithms

Classification of young stellar objects (YSOs) into different evolutionary stages helps us to understand the formation process of new stars and planetary systems. Such classification has traditionally been based on spectral energy distribution (SED) analysis. An alternative approach is provided by supervised machine learning algorithms, which can be trained to classify large samples of YSOs much faster than via SED analysis. We attempt to classify a sample of Orion YSOs (the parent sample size is 330) into different classes, where each source has already been classified using multiwavelength SED analysis. We used eight different learning algorithms to classify the target YSOs, namely a decision tree, random forest, gradient boosting machine (GBM), logistic regression, naïve Bayes classifier, k -nearest neighbour classifier, support vector machine, and neural network. The classifiers were trained and tested by using a 10-fold cross-validation procedure. As the learning features, we employed ten different continuum flux densities spanning from the near-infrared to submillimetre wavebands ( λ= 3.6870 ). With a classification accuracy of 82