Maximum Margin Interval Trees
نویسندگان
چکیده
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.
منابع مشابه
Univariate Decision Tree Induction using Maximum Margin Classification
In many pattern recognition applications, first decision trees are used due to their simplicity and easily interpretable nature. In this paper, we propose a new decision tree learning algorithm called univariate margin tree where, for each continuous attribute, the best split is found using convex optimization. Our simulation results on 47 data sets show that the novel margin tree classifier pe...
متن کاملOne-Sided Interval Trees
We give an alternative treatment and extension of some results of Itoh and Mahmoud on one-sided interval trees. The proofs are based on renewal theory, including a case with mixed multiplicative and additive renewals.
متن کاملThe Study of Different Water Regimes on Photosynthetic Performance and Leaf Water Status of Pistachio Trees (Pistacia vera L.)
Water deficiency is one of the most important environmental stresses that limit plant growth and crop production. Measurement of chlorophyll fluorescence parameters is considered as an important indicator to evaluate the photosynthetic apparatus. In the present study, the effects of regulated water deficit, investigated in four water-regimes in pistachio orchard with 12-year-old trees of Akbari...
متن کاملPrecise Statements of Convergence for AdaBoost and arc-gv
We present two main results, the first concerning Freund and Schapire’s AdaBoost algorithm, and the second concerning Breiman’s arc-gv algorithm. Our discussion of AdaBoost revolves around a circumstance called the case of “bounded edges”, in which AdaBoost’s convergence properties can be completely understood. Specifically, our first main result is that if AdaBoost’s “edge” values fall into a ...
متن کاملGeneralization Error and Algorithmic Convergence of Median Boosting
We have recently proposed an extension of ADABOOST to regression that uses the median of the base regressors as the final regressor. In this paper we extend theoretical results obtained for ADABOOST to median boosting and to its localized variant. First, we extend recent results on efficient margin maximizing to show that the algorithm can converge to the maximum achievable margin within a pres...
متن کامل