A Dissimilarity-Based Drift Detection Method

In real-world applications, machine learning algorithms can be employed in spam detection, environmental monitoring, fraud detection, medical diagnoses, among others. Most of these problems present an environment which changes over time. The problem involving classification tasks in dynamic environments has become one of the major challenges in machine learning domain in the last decades. Currently in the literature, methods based on accuracy monitoring are commonly used to detect changes explicitly. However, these methods may become infeasible in some real-world applications especially due to two aspects: they may need human operator feedback, and may depend on a significant decrease of accuracy to be able to detect changes. In addition, most of these methods are also incremental learning-based, since they update the decision model for every incoming example. However, this may lead to system unnecessary updates. In order to overcome these problems, this paper proposes a method to detect changes based on data distribution dissimilarity and to update the decision model only after drift detection. The achievement of this method is relevant to allow change detection and reaction be applicable in several practical problems. The experiments conducted indicate that the proposed method attains performance and detection rates similar to the traditional DDM and EDDM, which are incremental learning-based and performance monitoring-based drift detectors.