Search Results for author: Alex A. Freitas

Found 6 papers, 3 papers with code

Automated Machine Learning for Positive-Unlabelled Learning

1 code implementation12 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.

Bayesian Optimisation

Hierarchical Dependency Constrained Tree Augmented Naive Bayes Classifiers for Hierarchical Feature Spaces

no code implementations8 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.

A Robust Experimental Evaluation of Automated Multi-Label Classification Methods

1 code implementation16 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.

AutoML Bayesian Optimization +3

Multi-label classification search space in the MEKA software

1 code implementation28 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.

Classification General Classification +1

A New Hierarchical Redundancy Eliminated Tree Augmented Naive Bayes Classifier for Coping with Gene Ontology-based Features

no code implementations6 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.

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