• Publications
  • Influence
Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis
TLDR
This work argues for abandonment of NHST by exposing its fallacies and, more importantly, offer better - more sound and useful - alternatives for it.
Learning Bayesian Networks with Thousands of Variables
TLDR
A novel algorithm that effectively explores the space of possible parent sets of a node on the basis of an approximated score function that is computed in constant time and an improvement of an existing ordering-based algorithm for structure optimization.
Robust Feature Selection by Mutual Information Distributions
TLDR
A fast, newly defined method is shown to outperform the traditional approach based on empirical mutual information on a number of real data sets and a theoretical development is reported that allows one to efficiently extend the above methods to incomplete samples in an easy and effective way.
Learning Reliable Classifiers From Small or Incomplete Data Sets: The Naive Credal Classifier 2
TLDR
This paper shows that with missing values, empirical evaluations may not reliably estimate the accuracy of a traditional classifier, such as naive Bayes, and this phenomenon adds even more value to the robust approach to classification implemented by NCC2.
A Bayesian Wilcoxon signed-rank test based on the Dirichlet process
TLDR
This work proposes a nonparametric Bayesian version of the Wilcoxon signed-rank test using a Dirichlet process (DP) based prior, and addresses in two different ways the problem of how to choose the infinite dimensional parameter that characterizes the DP.
...
...