BoostTree and BoostForest for Ensemble Learning

Bootstrap aggregating (Bagging) and boosting are two popular ensemble learning approaches, which combine multiple base learners to generate a composite model for more accurate and more reliable performance. They have been widely used in biology, engineering, healthcare, etc. This article proposes BoostForest, which is an ensemble learning approach using BoostTree as base learners and can be used for both classification and regression. BoostTree constructs a tree model by gradient boosting. It achieves high randomness (diversity) by sampling its parameters randomly from a parameter pool, and selecting a subset of features randomly at node splitting. BoostForest further increases the randomness by bootstrapping the training data in constructing different BoostTrees. BoostForest outperformed four classical ensemble learning approaches (Random Forest, Extra-Trees, XGBoost and LightGBM) on 34 classification and regression datasets. Remarkably, BoostForest has only one hyper-parameter (the number of BoostTrees), which can be easily specified. Our code is publicly available, and the proposed ensemble learning framework can also be used to combine many other base learners

Study on Reverse Flotation Process of Magnesite and Dolomite in Dodecylamine System

Through the single mineral flotation test, the flotation behavior of magnesite and dolomite in the dodecylamine flotation system and the influence of the regulator Fe3+ on the floatability of the two minerals were studied. Based on Fourier Infrared Spectroscopy (FTIR) and contact angle test, the mechanism of Fe3+ was studied. The flotation test results show that in the dodecylamine system, under the natural pH condition of the pulp, the floatability of dolomite is better than that of magnesite. There is a certain floatation difference between magnesite and dolomite, and the addition of a small amount of Fe3+ significantly improves the floatation difference of the two minerals. Infrared spectroscopy test results show that the adsorption of dodecylamine on the surface of dolomite is mainly electrostatic adsorption, dolomite CO32− out-of-plane bending vibration and CO32− in-plane bending vibration are almost unchanged, and -CH3 and -CH2 are symmetrical and asymmetrical. The peak value of extensional vibration increases. The contact angle test results show that the addition of a small amount of Fe3+ enhances the hydrophobicity of dolomite, which has the effect of activating dolomite