Search Results for author:
Found 6 papers, 1 papers with code
A Bayesian Bradley-Terry model to compare multiple ML algorithms on multiple data sets
This paper proposes a Bayesian model to compare multiple algorithms on multiple data sets, on any metric.
How to tune the RBF SVM hyperparameters?: An empirical evaluation of 18 search algorithms
In general, the selection of the hyperparameters is a non-convex optimization problem and thus many algorithms have been proposed to solve it, among them: grid search, random search, Bayesian optimization, simulated annealing, particle swarm optimization, Nelder Mead, and others.
An empirical evaluation of imbalanced data strategies from a practitioner's point of view
The paper also examines a selection of newer algorithms within the categories of specialized algorithms, oversampling, and ensemble methods.
Nested cross-validation when selecting classifiers is overzealous for most practical applications
The usual approach is to apply a nested cross-validation procedure; hyperparameter selection is performed in the inner cross-validation, while the outer cross-validation computes an unbiased estimate of the expected accuracy of the algorithm \emph{with cross-validation based hyperparameter tuning}.
Specialized Support Vector Machines for Open-set Recognition
In the open-set scenario, however, a test sample can belong to none of the known classes and the classifier must properly reject it by classifying it as unknown.
Comparison of 14 different families of classification algorithms on 115 binary datasets
We tested 14 very different classification algorithms (random forest, gradient boosting machines, SVM - linear, polynomial, and RBF - 1-hidden-layer neural nets, extreme learning machines, k-nearest neighbors and a bagging of knn, naive Bayes, learning vector quantization, elastic net logistic regression, sparse linear discriminant analysis, and a boosting of linear classifiers) on 115 real life binary datasets.
