25 citations · 53 across the 8 of their papers we have counts for
5 papers · 1 filter
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…