1 code implementation • 12 Jan 2024 • Jack D. Saunders, Alex A. Freitas
Positive-Unlabelled (PU) learning is a growing field of machine learning that aims to learn classifiers from data consisting of labelled positive and unlabelled instances, which can be in reality positive or negative, but whose label is unknown.
no code implementations • 8 Feb 2022 • Cen Wan, Alex A. Freitas
The Tree Augmented Naive Bayes (TAN) classifier is a type of probabilistic graphical model that constructs a single-parent dependency tree to estimate the distribution of the data.
no code implementations • 16 Sep 2020 • Márcio P. Basgalupp, Rodrigo C. Barros, Alex G. C. de Sá, Gisele L. Pappa, Rafael G. Mantovani, André C. P. L. F. de Carvalho, Alex A. Freitas
Auto-WEKA combines algorithm selection and hyper-parameter optimisation to recommend classification algorithms from multiple paradigms.
1 code implementation • 16 May 2020 • Alex G. C. de Sá, Cristiano G. Pimenta, Gisele L. Pappa, Alex A. Freitas
In this work, we provide a general comparison of five automated multi-label classification methods -- two evolutionary methods, one Bayesian optimization method, one random search and one greedy search -- on 14 datasets and three designed search spaces.
1 code implementation • 28 Nov 2018 • Alex G. C. de Sá, Cristiano G. Pimenta, Gisele L. Pappa, Alex A. Freitas
This supplementary material aims to describe the proposed multi-label classification (MLC) search spaces based on the MEKA and WEKA softwares.
no code implementations • 6 Jul 2016 • Cen Wan, Alex A. Freitas
The Tree Augmented Naive Bayes classifier is a type of probabilistic graphical model that can represent some feature dependencies.