Log In Sign Up

Reservoir of Diverse Adaptive Learners and Stacking Fast Hoeffding Drift Detection Methods for Evolving Data Streams

by   Ali Pesaranghader, et al.

The last decade has seen a surge of interest in adaptive learning algorithms for data stream classification, with applications ranging from predicting ozone level peaks, learning stock market indicators, to detecting computer security violations. In addition, a number of methods have been developed to detect concept drifts in these streams. Consider a scenario where we have a number of classifiers with diverse learning styles and different drift detectors. Intuitively, the current 'best' (classifier, detector) pair is application dependent and may change as a result of the stream evolution. Our research builds on this observation. We introduce the Tornado framework that implements a reservoir of diverse classifiers, together with a variety of drift detection algorithms. In our framework, all (classifier, detector) pairs proceed, in parallel, to construct models against the evolving data streams. At any point in time, we select the pair which currently yields the best performance. We further incorporate two novel stacking-based drift detection methods, namely the FHDDMS and FHDDMS_add approaches. The experimental evaluation confirms that the current 'best' (classifier, detector) pair is not only heavily dependent on the characteristics of the stream, but also that this selection evolves as the stream flows. Further, our FHDDMS variants detect concept drifts accurately in a timely fashion while outperforming the state-of-the-art.


page 32

page 33

page 35

page 36


CURIE: A Cellular Automaton for Concept Drift Detection

Data stream mining extracts information from large quantities of data fl...

Recurring Concept Meta-learning for Evolving Data Streams

When concept drift is detected during classification in a data stream, a...

Soft Confusion Matrix Classifier for Stream Classification

In this paper, the issue of tailoring the soft confusion matrix (SCM) ba...

Automatic Learning to Detect Concept Drift

Many methods have been proposed to detect concept drift, i.e., the chang...

On the Reliable Detection of Concept Drift from Streaming Unlabeled Data

Classifiers deployed in the real world operate in a dynamic environment,...

Evaluating k-NN in the Classification of Data Streams with Concept Drift

Data streams are often defined as large amounts of data flowing continuo...

McDiarmid Drift Detection Methods for Evolving Data Streams

Increasingly, Internet of Things (IoT) domains, such as sensor networks,...