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KDD
2009
ACM
224views Data Mining» more  KDD 2009»
14 years 3 days ago
Issues in evaluation of stream learning algorithms
Learning from data streams is a research area of increasing importance. Nowadays, several stream learning algorithms have been developed. Most of them learn decision models that c...
João Gama, Raquel Sebastião, Pedro P...
JMLR
2010
154views more  JMLR 2010»
13 years 2 months ago
MOA: Massive Online Analysis
Massive Online Analysis (MOA) is a software environment for implementing algorithms and running experiments for online learning from evolving data streams. MOA includes a collecti...
Albert Bifet, Geoff Holmes, Richard Kirkby, Bernha...
JMLR
2010
130views more  JMLR 2010»
13 years 2 months ago
MOA: Massive Online Analysis, a Framework for Stream Classification and Clustering
Massive Online Analysis (MOA) is a software environment for implementing algorithms and running experiments for online learning from evolving data streams. MOA is designed to deal...
Albert Bifet, Geoff Holmes, Bernhard Pfahringer, P...
SAC
2004
ACM
14 years 29 days ago
Forest trees for on-line data
This paper presents an hybrid adaptive system for induction of forest of trees from data streams. The Ultra Fast Forest Tree system (UFFT) is an incremental algorithm, with consta...
João Gama, Pedro Medas, Ricardo Rocha
ICAPR
2009
Springer
14 years 2 months ago
Relevant and Redundant Feature Analysis with Ensemble Classification
— Feature selection and ensemble classification increase system efficiency and accuracy in machine learning, data mining and biomedical informatics. This research presents an ana...
Rakkrit Duangsoithong, Terry Windeatt