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20162024
most citedVariable selection for Naïve Bayes classification

86 citations · 330 across the 9 of their papers we have counts for

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5 papers · 1 filter

math.OC2024

A Unified Approach to Extract Interpretable Rules from Tree Ensembles via Integer Programming

Lorenzo Bonasera, Emilio Carrizosa

Tree ensembles are very popular machine learning models, known for their effectiveness in supervised classification and regression tasks. Their performance derives from aggregating…

q-fin.RM2024

Analysis of an aggregate loss model in a Markov renewal regime

Pepa Ramírez-Cobo, Emilio Carrizosa, Rosa Elvira Lillo

In this article we consider an aggregate loss model with dependent losses. The losses occurrence process is governed by a two-state Markovian arrival process (MAP2), a Markov renew…

stat.ML202486 cited

Variable selection for Naïve Bayes classification

Rafael Blanquero, Emilio Carrizosa, Pepa Ramírez-Cobo +1

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 Na…

stat.ME202411 cited

A cost-sensitive constrained Lasso

Rafael Blanquero, Emilio Carrizosa, Pepa Ramírez-Cobo +1

The Lasso has become a benchmark data analysis procedure, and numerous variants have been proposed in the literature. Although the Lasso formulations are stated so that overall pre…

stat.ML202454 cited

Cost-sensitive Feature Selection for Support Vector Machines

Sandra Benítez-Peña, Rafael Blanquero, Emilio Carrizosa +1

Feature Selection is a crucial procedure in Data Science tasks such as Classification, since it identifies the relevant variables, making thus the classification procedures more in…