Constraint learning based gradient boosting trees

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 any boosting technique. We implemented the algorithms by modifying the gradient boosting trees (GBT) model, and we also introduced two measures for evaluating the models that were trained to solve the constraint problems. We compared the proposed algorithms to three baseline algorithms using four real-life datasets. Due to the algorithms’ focus on satisfying the constraints, in most cases the results showed significant improvement in the constraint-related measures, with just a minimal effect on the general prediction error. The main impact of the proposed approach is in its ability to derive a model with a higher level of assurance for specific cases of interest (i.e., the constrained cases). This is extremely important and has great significance in various use cases and expert and intelligent systems, particularly critical systems, such as critical healthcare systems (e.g., when predicting blood pressure or blood sugar level), safety systems (e.g., when aiming to estimate the distance of cars or airplanes from other objects), or critical industrial systems (e.g., require to estimate their usability along time). In each of these cases, there is a subpopulation of all instances that is of greater interest to the expert or system, and the sensitivity of the model’s error changes according to the real value of the predicted feature. For example, for a subpopulation of patients (e.g., patients under the age of eight, or patients known to be at risk), physicians often require a sensitive model that accurately predicts blood pressure values.