In data stream classification, the most used evaluation is the prequential one, where instances are first used to test, and then to train. However, the weakness of prequential evaluation compared to cross-validation was that it was running only one experiment.

We are proud to announce that MOA now contains a new prequential cross-validation evaluation with the advantages of prequential evaluation and the advantages of cross-validation evaluation. The new task is called EvaluatePrequentialCV:

Other new techniques added are:

  • AdwinClassificationPerformanceEvaluator: new performance evaluator that uses an adaptive size sliding window to estimate accuracy on real time.
  • Kappa M measure: a new measure that compares with a majority class classifier and that in streaming is more appropriate than the standard Kappa statistic.

 

Reference

Albert Bifet, Gianmarco De Francisci Morales, Jesse Read, Geoff Holmes, Bernhard Pfahringer: Efficient Online Evaluation of Big Data Stream Classifiers. KDD 2015: 59-68