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Found 11 papers, 0 papers with code
On Predictive Explanation of Data Anomalies
PROTEUS is designed to return an accurate estimate of out-of-sample predictive performance to serve as a metric of the quality of the approximation.
Inference of Stochastic Dynamical Systems from Cross-Sectional Population Data
Inferring the driving equations of a dynamical system from population or time-course data is important in several scientific fields such as biochemistry, epidemiology, financial mathematics and many others.
A generalised OMP algorithm for feature selection with application to gene expression data
Feature selection for predictive analytics is the problem of identifying a minimal-size subset of features that is maximally predictive of an outcome of interest.
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.
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).
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.
Feature Selection with the R Package MXM: Discovering Statistically-Equivalent Feature Subsets
The statistically equivalent signature (SES) algorithm is a method for feature selection inspired by the principles of constrained-based learning of Bayesian Networks.
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.
Discovering and Exploiting Entailment Relationships in Multi-Label Learning
Marginal probabilities are entered as soft evidence in the network and adjusted through probabilistic inference.
Constraint-based Causal Discovery from Multiple Interventions over Overlapping Variable Sets
In this work, we present algorithm COmbINE, which accepts a collection of data sets over overlapping variable sets under different experimental conditions; COmbINE then outputs a summary of all causal models indicating the invariant and variant structural characteristics of all models that simultaneously fit all of the input data sets.
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.
