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Maximum Margin Interval Trees
Alexandre Drouin · Toby Hocking · Francois Laviolette

Mon Dec 04 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #44

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.

Author Information

Alexandre Drouin (Element AI)
Toby Hocking (McGill Genome Center, McGill University)
Francois Laviolette (Université Laval)

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