2011
DOI: 10.1109/tkde.2010.61
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Classification and Novel Class Detection in Concept-Drifting Data Streams under Time Constraints

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Cited by 359 publications
(332 citation statements)
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“…We are not aware of any good guidance with respect to change detection under different change scenarios in the unsupervised learning context. The majority of current works in data streams (Bifet et al 2010;Masud et al 2011;Duarte and Gama 2014) focus on supervised learning.…”
Section: Implications and Potential Future Workmentioning
confidence: 99%
“…We are not aware of any good guidance with respect to change detection under different change scenarios in the unsupervised learning context. The majority of current works in data streams (Bifet et al 2010;Masud et al 2011;Duarte and Gama 2014) focus on supervised learning.…”
Section: Implications and Potential Future Workmentioning
confidence: 99%
“…In addition, characteristics of data may change over time (concept drift). Here, supervised and unsupervised learning need to be adaptive to cope with changes [27,28,29,30]. There are two ways adaptive learning can be developed.…”
Section: Related Workmentioning
confidence: 99%
“…There are two ways adaptive learning can be developed. One is incremental learning [30] and the other one is ensemble-based learning [27,28,29]. Here is an example for incremental learning in user action prediction.…”
Section: Related Workmentioning
confidence: 99%
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“…Another issue of considerable recent interest is that of conceptevolution, which refers to the emergence of a new class. Several approaches have been proposed to address this issue [8], [9]. These approaches pro-actively detect novel classes in the stream, before being trained with the novel class instances.…”
Section: Introductionmentioning
confidence: 99%