Maximum Margin Interval Trees
This addresses the problem of handling censored or interval-valued data in fields like genomics and medicine, offering a nonlinear alternative to existing linear models.
The paper tackles learning nonlinear regression trees for interval-valued output data, achieving state-of-the-art speed and prediction accuracy in benchmarks.
Learning a regression function using censored or interval-valued output data is an important problem in fields such as genomics and medicine. The goal is to learn a real-valued prediction function, and the training output labels indicate an interval of possible values. Whereas most existing algorithms for this task are linear models, in this paper we investigate learning nonlinear tree models. We propose to learn a tree by minimizing a margin-based discriminative objective function, and we provide a dynamic programming algorithm for computing the optimal solution in log-linear time. We show empirically that this algorithm achieves state-of-the-art speed and prediction accuracy in a benchmark of several data sets.