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Found 6 papers, 1 papers with code
A Meta-Level Learning Algorithm for Sequential Hyper-Parameter Space Reduction in AutoML
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.
Massively-Parallel Feature Selection for Big Data
We present the Parallel, Forward-Backward with Pruning (PFBP) algorithm for feature selection (FS) in Big Data settings (high dimensionality and/or sample size).
Bootstrapping the Out-of-sample Predictions for Efficient and Accurate Cross-Validation
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.
Forward-Backward Selection with Early Dropping
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.
Scoring and Searching over Bayesian Networks with Causal and Associative Priors
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.
Scoring and Searching over Bayesian Networks with Causal and Associative Priors
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.
