Classification systems in dynamic environments: an overview

Abstract

Data mining and machine learning algorithms can be employed to perform a variety of tasks. However, since most of these problems may depend on environments that change over time, performing classification tasks in dynamic environments has been a challenge in data mining research domain in the last decades. Currently, in the literature, the most common strategies used to detect changes are based on accuracy monitoring, which relies on previous knowledge of the data in order to identify whether or not correct classifications are provided. However, such a feedback can be infeasible in practical problems. In this work, we present a comprehensive overview of current machine learning/data mining approaches proposed to deal with dynamic environments problems. The objective is to highlight the main drawbacks and open issues, as well as future directions and problems worthy of investigation. In addition, we provide the definitions of the main terms used to represent this problem in the literature, such as concept drift and novelty detection. WIREs Data Mining Knowl Discov 2016, 6:156-166. doi: 10.1002/widm.1184 For further resources related to this article, please visit the .