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 …

Many real-life applications consider nominal categorical predictor variables that have a
hierarchical structure, eg economic activity data in Official Statistics. In this paper, we focus …

Since the seminal paper by Bates and Granger in 1969, a vast number of ensemble
methods that combine different base regressors to generate a unique one have been …

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 …

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 …

Abstract The Naïve Bayes is a tractable and efficient approach for statistical classification. In
general classification problems, the consequences of misclassifications may be rather …

Bias in data may lead to prediction procedures which discriminate individuals from sensitive
groups. In this paper we propose a Bayesian method for parameter estimation in the linear …