  We present SHEEP, a new machine learning approach to the classic problem of
astronomical source classification, which combines the outputs from the
XGBoost, LightGBM, and CatBoost learning algorithms to create stronger
classifiers. A novel step in our pipeline is that prior to performing the
classification, SHEEP first estimates photometric redshifts, which are then
placed into the data set as an additional feature for classification model
training; this results in significant improvements in the subsequent
classification performance. SHEEP contains two distinct classification
methodologies: (i) Multi-class and (ii) one versus all with correction by a
meta-learner. We demonstrate the performance of SHEEP for the classification of
stars, galaxies, and quasars using a data set composed of SDSS and WISE
photometry of 3.5 million astronomical sources. The resulting F1-scores are as
follows: 0.992 for galaxies; 0.967 for quasars; and 0.985 for stars. In terms
of the F1-scores for the three classes, SHEEP is found to outperform a recent
RandomForest-based classification approach using an essentially identical data
set. Our methodology also facilitates model and data set explainability via
feature importances; it also allows the selection of sources whose uncertain
classifications may make them interesting sources for follow-up observations.
