Constraint learning based gradient boosting trees

Abraham Israeli, Lior Rokach, Asaf Shabtai

Expert Systems with Applications 128, 287-300, 2019

Predictive regression models aim to find the most accurate solution to a given problem, often without any constraints related to the model’s predicted values. Such constraints have been used in prior research where they have been applied to a subpopulation within the training dataset which is of greater interest and importance. In this research we introduce a new setting of regression problems, in which each instance can be assigned a different constraint, defined based on the value of the target (predicted) attribute. The new use of constraints is taken into account and incorporated into the learning process, and is also considered when evaluating the induced model. We propose two algorithms which are modifications to the regression boosting method. There are two advantages of the proposed algorithms: they are not dependent on the base learner used during the learning process, and they can be adopted by …