Self-Adaptive Ensemble Classifier for Handling Complex Concept Drift

Method: Adaptive Random Forest classifier with ADWIN change detector is used for detecting change in a data stream and adapting to drift detection in the streamed data

We showed how various forms of semantic change can be modeled using lemon to link lexical entries to concepts in ontologies and using contexts to define how a specific concept was

In case of interval pattern structures, by its design, the algorithm guarantees to classify each test instance with at least as good rule (in terms of an information criterion) as

This is a real challenge because usually approaches adapted to relational data have a non-linear complexity regarding the number of objects in the data-set, which is not adapted to

The method uses two windows to detect the change: (1) W ref contains the distribution of X i at the beginning of the stream and is used as a reference window; (2) W cur a

As the performance evaluation of these three ensemble classifiers did not reveal a significant level of accuracy for classifying the region of interest (especially for the

1) Fix the minimum classification performance p which is always required to the diagnostic system and a fraction *  indicating the maximum performance reduction

This paper presented a model that uses an ensemble of neural networks trained by a quantum-inspired evolutionary algorithm to learn data sets (possibly with concept drifts)