6533b86dfe1ef96bd12ca96b

RESEARCH PRODUCT

On the Online Classification of Data Streams Using Weak Estimators

B. John OommenHanane TavasoliAnis Yazidi

subject

Complex data typeTraining setLearning automataComputer sciencebusiness.industryData stream miningEstimator020206 networking & telecommunications02 engineering and technologycomputer.software_genreMachine learning0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingData miningArtificial intelligencebusinesscomputerClassifier (UML)Juncture

description

In this paper, we propose a novel online classifier for complex data streams which are generated from non-stationary stochastic properties. Instead of using a single training model and counters to keep important data statistics, the introduced online classifier scheme provides a real-time self-adjusting learning model. The learning model utilizes the multiplication-based update algorithm of the Stochastic Learning Weak Estimator (SLWE) at each time instant as a new labeled instance arrives. In this way, the data statistics are updated every time a new element is inserted, without requiring that we have to rebuild its model when changes occur in the data distributions. Finally, and most importantly, the model operates with the understanding that the correct classes of previously-classified patterns become available at a later juncture subsequent to some time instances, thus requiring us to update the training set and the training model.

https://doi.org/10.1007/978-3-319-42007-3_7