1 code implementation • 11 Dec 2023 • Giorgos Borboudakis, Paulos Charonyktakis, Konstantinos Paraschakis, Ioannis Tsamardinos
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 selection, and modelling.
no code implementations • 23 Aug 2017 • Ioannis Tsamardinos, Giorgos Borboudakis, Pavlos Katsogridakis, Polyvios Pratikakis, Vassilis Christophides
We present the Parallel, Forward-Backward with Pruning (PFBP) algorithm for feature selection (FS) in Big Data settings (high dimensionality and/or sample size).
no code implementations • 23 Aug 2017 • Ioannis Tsamardinos, Elissavet Greasidou, Michalis Tsagris, Giorgos Borboudakis
BBC-CV's main idea is to bootstrap the whole process of selecting the best-performing configuration on the out-of-sample predictions of each configuration, without additional training of models.
no code implementations • 30 May 2017 • Giorgos Borboudakis, Ioannis Tsamardinos
In experiments we show that the proposed heuristic increases computational efficiency by about two orders of magnitude in high-dimensional problems, while selecting fewer variables and retaining predictive performance.
no code implementations • 9 Aug 2014 • 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 complementing the data.
no code implementations • 28 Sep 2012 • 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 complementing the data.