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An Overview on Concepts Drift Learning
(Ieee-inst Electrical Electronics Engineers Inc, 2019-01-01)
Concept drift techniques aim at learning patterns from data streams that may change over time. Although such behavior is not usually expected in controlled environments, real-world scenarios can face changes in the data, ...
Particle Competition and Cooperation in Networks for Semi-Supervised Learning with Concept Drift
(IEEE, 2012-01-01)
Concept drift is a problem of increasing importance in machine learning and data mining. Data sets under analysis are no longer only static databases, but also data streams in which concepts and data distributions may not ...
A stable and online approach to detect concept drift in data streams
(Universidade de São Paulo - USPUniversidade Federal de São Carlos - UFSCarCentro de Robótica de São Carlos - CROBSociedade Brasileira de Computação - SBCSociedade Brasileira de Automática - SBASão Carlos, 2014-10)
The detection of concept drift allows to point out when a data stream changes its behavior over time, what supports further analysis to understand why the phenomenon represented by such data has changed. Nowadays, researchers ...
Learning concept drift with ensembles of optimum-path forest-based classifiers
(Elsevier B.V., 2019-06-01)
Concept drift methods learn patterns in non-stationary environments. Although such behavior is usually not expected in traditional classification problems, in real-world scenarios one can face them very much easier. In ...
A survey on machine learning for recurring concept drifting data streams
The problem of concept drift has gained a lot of attention in recent years. This aspect is key in many domains exhibiting non-stationary as well as cyclic patterns and structural breaks affecting their generative processes. ...
Particle Competition and Cooperation in Networks for Semi-Supervised Learning with Concept Drift
(IEEE, 2012-01-01)
Concept drift is a problem of increasing importance in machine learning and data mining. Data sets under analysis are no longer only static databases, but also data streams in which concepts and data distributions may not ...
IGMM-CD: a gaussian mixture classification algorithm for data streams with concept drifts
(Universidade Federal do Rio Grande do Norte – UFRNSociedade Brasileira de Computação – SBCNatal, 2015-11)
Learning concepts from data streams differs significantly from traditional batch learning, because in data streams the concepts to be learned may evolve over time. Incremental learning paradigm is a promising approach for ...