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Article
On sparse optimal regression trees
(Elsevier, 2021-12-18)
In this paper, we model an optimal regression tree through a continuous optimization problem, where a compromise between prediction accuracy and both types of sparsity, namely local and global, is sought. Our approach can ...
Article
A discretizing algorithm for location problems
(ELSEVIER SCIENCE BV, 1995)
A new and simple methodology is proposed to solve both constrained and unconstrained planar continuous single-facility location problems. As particular instances, the classical location problems with mixed gauges can be ...
Article
On approximate Monetary Unit Sampling
(ELSEVIER SCIENCE BV, 2011-09-28)
Monetary Unit Sampling (MUS), also known as Dollar-Unit Sampling, is a popular sampling strategy in Auditing, in which all units are to be randomly selected with probabilities proportional to the book value. However, if ...
Article
Sparsity in optimal randomized classification trees
(ELSEVIER SCIENCE BV, 2019-12-16)
Decision trees are popular Classification and Regression tools and, when small-sized, easy to interpret. Traditionally, a greedy approach has been used to build the trees, yielding a very fast training process; however, ...
Article
Gaussian variable neighborhood search for continuous optimization
(PERGAMON-ELSEVIER SCIENCE LTD, 2012-01-01)
Variable Neighborhood Search (VNS) has shown to be a powerful tool for solving both discrete and box-constrained continuous optimization problems. In this note we extend the methodology by allowing also to address unconstrained ...
Article
On optimal regression trees to detect critical intervals for multivariate functional data
(ScienceDirect, 2023-01-13)
In this paper, we tailor optimal randomized regression trees to handle multivariate functional data. A compromise between prediction accuracy and sparsity is sought. Whilst fitting the tree model, the detection of a reduced ...