Optimizing the Parameters of Drift Detection Methods using a Genetic Algorithm

Extracting knowledge from environments with a continuous flow of data (data streams) is progressively receiving more attention. In such environments, the data distribution usually changes over time, which is known as concept drift. This paper presents a genetic algorithm aimed at adjusting the parameters of concept drift detection methods to improve their accuracies. Experiments were performed with four drift detectors, comparing their results using the values as presented by their original proposals to those using the average of the values returned by the genetic algorithm on multiple datasets containing the same type of concept drifts. Tests were performed in nine artificial datasets, each one with abrupt, slow gradual, and fast gradual concept drifts versions, as well as three real-world datasets. Results indicate that the predictive accuracies statistically increased in many situations.