2017
Autores
Oliveira, Eugenio; Gama, Joao; Vale, ZitaA.; Cardoso, HenriqueLopes;
Publicação
EPIA
Abstract
2017
Autores
Gama, J; Oliveira, E; Cardoso, HL;
Publicação
NEW GENERATION COMPUTING
Abstract
2017
Autores
Gama, J;
Publicação
Encyclopedia of Machine Learning and Data Mining
Abstract
2017
Autores
Duarte, J; Gama, J;
Publicação
Proceedings of the Symposium on Applied Computing, SAC 2017, Marrakech, Morocco, April 3-7, 2017
Abstract
Feature selection and feature ranking are two aspects of the same learning task. They are well studied in batch scenarios, but not in the streaming setting. This paper presents a study on feature ranking from data streams in online learning regression models. The main challenge here is the relevance of features might change over time: features relevant in the past might be irrelevant now and vice-versa. We propose three new online feature ranking algorithms designed for Hoeffding algorithms. We have implemented the three methods in AMRules, a streaming regression algorithm to learn model rules. We compare their behaviour experimentally and present the pros and cons of each method. Copyright 2017 ACM.
2017
Autores
Oliveira, E; Cardoso, HL; Gama, J; Vale, Z;
Publicação
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Abstract
2017
Autores
Oliveira, E; Gama, J; Vale, Z; Lopes Cardoso, H;
Publicação
Lecture Notes in Computer Science
Abstract
The access to the final selection minute is only available to applicants.
Please check the confirmation e-mail of your application to obtain the access code.