61 citations · 150 across the 7 of their papers we have counts for
7 papers
A Bayesian Bradley-Terry model to compare multiple ML algorithms on multiple data sets
Jacques Wainer
This paper proposes a Bayesian model to compare multiple algorithms on multiple data sets, on any metric. The model is based on the Bradley-Terry model, that counts the number of t…
How to tune the RBF SVM hyperparameters?: An empirical evaluation of 18 search algorithms
Jacques Wainer, Pablo Fonseca
SVM with an RBF kernel is usually one of the best classification algorithms for most data sets, but it is important to tune the two hyperparameters and to the data itself.…
An empirical evaluation of imbalanced data strategies from a practitioner's point of view
Jacques Wainer
This paper evaluates six strategies for mitigating imbalanced data: oversampling, undersampling, ensemble methods, specialized algorithms, class weight adjustments, and a no-mitiga…
Nested cross-validation when selecting classifiers is overzealous for most practical applications
Jacques Wainer, Gavin Cawley
When selecting a classification algorithm to be applied to a particular problem, one has to simultaneously select the best algorithm for that dataset \emph{and} the best set of hyp…
Comparison of 14 different families of classification algorithms on 115 binary datasets
Jacques Wainer
We tested 14 very different classification algorithms (random forest, gradient boosting machines, SVM - linear, polynomial, and RBF - 1-hidden-layer neural nets, extreme learning m…
Open-Set Support Vector Machines
Pedro Ribeiro Mendes Júnior, Terrance E. Boult, Jacques Wainer +1
Often, when dealing with real-world recognition problems, we do not need, and often cannot have, knowledge of the entire set of possible classes that might appear during operationa…