25 citations · 53 across the 8 of their papers we have counts for
8 papers
Confidence Interval Estimation of Predictive Performance in the Context of AutoML
Konstantinos Paraschakis, Andrea Castellani, Giorgos Borboudakis +1
Any supervised machine learning analysis is required to provide an estimate of the out-of-sample predictive performance. However, it is imperative to also provide a quantification…
A Meta-Level Learning Algorithm for Sequential Hyper-Parameter Space Reduction in AutoML
Giorgos Borboudakis, Paulos Charonyktakis, Konstantinos Paraschakis +1
AutoML platforms have numerous options for the algorithms to try for each step of the analysis, i.e., different possible algorithms for imputation, transformations, feature selecti…
Bootstrapping the Out-of-sample Predictions for Efficient and Accurate Cross-Validation
Ioannis Tsamardinos, Elissavet Greasidou, Michalis Tsagris +1
Cross-Validation (CV), and out-of-sample performance-estimation protocols in general, are often employed both for (a) selecting the optimal combination of algorithms and values of…
Massively-Parallel Feature Selection for Big Data
Ioannis Tsamardinos, Giorgos Borboudakis, Pavlos Katsogridakis +2
We present the Parallel, Forward-Backward with Pruning (PFBP) algorithm for feature selection (FS) in Big Data settings (high dimensionality and/or sample size). To tackle the chal…
Forward-Backward Selection with Early Dropping
Giorgos Borboudakis, Ioannis Tsamardinos
Forward-backward selection is one of the most basic and commonly-used feature selection algorithms available. It is also general and conceptually applicable to many different types…
Scoring and Searching over Bayesian Networks with Causal and Associative Priors
Giorgos Borboudakis, Ioannis Tsamardinos
A significant theoretical advantage of search-and-score methods for learning Bayesian Networks is that they can accept informative prior beliefs for each possible network, thus com…
Scoring and Searching over Bayesian Networks with Causal and Associative Priors
Giorgos Borboudakis, Ioannis Tsamardinos
A significant theoretical advantage of search-and-score methods for learning Bayesian Networks is that they can accept informative prior beliefs for each possible network, thus com…
Incorporating Causal Prior Knowledge as Path-Constraints in Bayesian Networks and Maximal Ancestral Graphs
Giorgos Borboudakis, Ioannis Tsamardinos
We consider the incorporation of causal knowledge about the presence or absence of (possibly indirect) causal relations into a causal model. Such causal relations correspond to dir…