Technique Analysis for Multilayer Perceptrons to Deal with Concept Drift in Data Streams
This paper describes how to use a multilayer perceptron to improve concept drift recovery in streaming environments.
Classifying instances in a data stream environment with concept drift is a challenging topic. The base learner must be adapted online to the current data. Several data mining algorithms have been adapted/used to this type of environment. In this study, two techniques are used to speed up the adaptation of an artificial neural network to the current data, increasing its predictive accuracy while detecting the concept drift sooner.
Experiments were performed to analyze how some techniques behave in different scenarios and compare them with other classifiers built to deal with data streams and concept drifts.
This study suggests two techniques to improve the classification results: an embedded concept drift detection method to identify when a change has occurred and setting the learning rate to a higher level whenever a new concept is being learned to give more weight to recent instances, with its value decreased over time.
Results indicate that gradually reducing the learning rate with an embedded concept drift detector has better statistical results than other single classifiers built to deal with data streams and concept drifts.
Based on the empirical results, this study provides recommendations on how to improve the multilayer perceptron in data stream environments suffering from concept drifts.
Researchers should conduct investigations to increase the number of base classifiers used in data stream environments and in situations where concept drifts occur.
The objective of this study is to increase the use of multilayer perceptrons in data stream environments suffering from concept drifts, as nowadays, Hoeffding Trees and Naive Bayes are the base classifiers mostly used.
Additional research includes adapting the online learning rate by increasing/decreasing it based on the performance of the Multilayer Perceptron. This scheme would allow the removal of parameters that must be set by the user, like learning rate upper bound and number of instances to return to the stable value.
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