Classification and regression trees, as well as their variants, are off-the-shelf methods in
Machine Learning. In this paper, we review recent contributions within the Continuous …

Abstract The Naïve Bayes has proven to be a tractable and efficient method for classification
in multivariate analysis. However, features are usually correlated, a fact that violates the …

When classification methods are applied to high-dimensional data, selecting a subset of the
predictors may lead to an improvement in the predictive ability of the estimated model, in …

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 …

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. The motivation of this …

Hedging against price increases is particularly important in times of significant market
uncertainty and price volatility. For commodity procuring firms, futures contracts are a …

FunctionalData Analysis (FDA) has become a very important field in recent years due to its
wide range of applications. However, there are several real-life applications in which hybrid …

Feature selection is a recurrent research topic in modern regression analysis, which strives
to build interpretable models, using sparsity as a proxy, without sacrificing predictive power …

A multiple objective space-time forecasting approach is presented involving cyclical curve
log-regression, and multivariate time series spatial residual correlation analysis. Specifically …

Given the increasing amounts of data and high feature dimensionalities in forecasting
problems, it is challenging to build regression models that are both computationally efficient …