7 citations · 18 across the 5 of their papers we have counts for
5 papers
Evaluating the Predictive Performance of Positive-Unlabelled Classifiers: a brief critical review and practical recommendations for improvement
Jack D. Saunders, Alex, A. Freitas
Positive-Unlabelled (PU) learning is a growing area of machine learning that aims to learn classifiers from data consisting of labelled positive and unlabelled instances. Whilst mu…
An Extensive Experimental Evaluation of Automated Machine Learning Methods for Recommending Classification Algorithms (Extended Version)
Márcio P. Basgalupp, Rodrigo C. Barros, Alex G. C. de Sá +4
This paper presents an experimental comparison among four Automated Machine Learning (AutoML) methods for recommending the best classification algorithm for a given input dataset.…
A Robust Experimental Evaluation of Automated Multi-Label Classification Methods
Alex G. C. de Sá, Cristiano G. Pimenta, Gisele L. Pappa +1
Automated Machine Learning (AutoML) has emerged to deal with the selection and configuration of algorithms for a given learning task. With the progression of AutoML, several effect…
Multi-label classification search space in the MEKA software
Alex G. C. de Sá, Cristiano G. Pimenta, Gisele L. Pappa +1
This supplementary material aims to describe the proposed multi-label classification (MLC) search spaces based on the MEKA and WEKA softwares. First, we overview 26 MLC algorithms…
A New Hierarchical Redundancy Eliminated Tree Augmented Naive Bayes Classifier for Coping with Gene Ontology-based Features
Cen Wan, Alex A. Freitas
The Tree Augmented Naive Bayes classifier is a type of probabilistic graphical model that can represent some feature dependencies. In this work, we propose a Hierarchical Redundanc…