no code implementations • 18 Nov 2022 • Víctor Blanco, Alberto Japón, Justo Puerto, Peter Zhang
In this paper, we introduce Optimal Classification Forests, a new family of classifiers that takes advantage of an optimal ensemble of decision trees to derive accurate and interpretable classifiers.
no code implementations • 16 Nov 2021 • Víctor Blanco, Alberto Japón, Justo Puerto
In this paper we present a novel mathematical optimization-based methodology to construct tree-shaped classification rules for multiclass instances.
no code implementations • 15 Dec 2020 • Víctor Blanco, Alberto Japón, Justo Puerto
In this paper we propose a novel methodology to construct Optimal Classification Trees that takes into account that noisy labels may occur in the training sample.
1 code implementation • 3 Dec 2020 • Víctor Blanco, Ricardo Gázquez, Marina Leal
The mathematical programming models that we provide are stochastic and multiperiod and we provide different robust objective functions.
Optimization and Control 91B32, 90C15, 90C10, 90B15
no code implementations • 21 Apr 2020 • Víctor Blanco, Alberto Japón, Justo Puerto
In this paper we propose novel methodologies to construct Support Vector Machine -based classifiers that takes into account that label noises occur in the training sample.
no code implementations • 22 Oct 2018 • Víctor Blanco, Alberto Japón, Justo Puerto
In this paper, we present a novel approach to construct multiclass classifiers by means of arrangements of hyperplanes.