GP under streaming data constraints: a case for pareto archiving?
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Data stream classification with artificial endocrine system
Applied Intelligence
GARF: towards self-optimised random forests
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
Benchmarking pareto archiving heuristics in the presence of concept drift: diversity versus age
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Dynamic multi-objective evolution of classifier ensembles for video face recognition
Applied Soft Computing
Intelligence for the personal web
The Personal Web
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We consider the problem of data stream classification, where the data arrive in a conceptually infinite stream, and the opportunity to examine each record is brief. We introduce a stream classification algorithm that is online, running in amortized {\cal O}(1) time, able to handle intermittent arrival of labeled records, and able to adjust its parameters to respond to changing class boundaries (“concept drift”) in the data stream. In addition, when blocks of labeled data are short, the algorithm is able to judge internally whether the quality of models updated from them is good enough for deployment on unlabeled records, or whether further labeled records are required. Unlike most proposed stream-classification algorithms, multiple target classes can be handled. Experimental results on real and synthetic data show that accuracy is comparable to a conventional classification algorithm that sees all of the data at once and is able to make multiple passes over it.