Fuzzy competence model drift detection for data-driven decision support systems

© 2017 Elsevier B.V. This paper focuses on concept drift in business intelligence and data-driven decision support systems (DSSs). The assumption of a fixed distribution in the data renders conventional static DSSs inaccurate and unable to make correct decisions when concept drift occurs. However, it is important to know when, how, and where concept drift occurs so a DSS can adjust its decision processing knowledge to adapt to an ever-changing environment at the appropriate time. This paper presents a data distribution-based concept drift detection method called fuzzy competence model drift detection (FCM-DD). By introducing fuzzy sets theory and replacing crisp boundaries with fuzzy ones, we have improved the competence model to provide a better, more refined empirical distribution of the data stream. FCM-DD requires no prior knowledge of the underlying distribution and provides statistical guarantee of the reliability of the detected drift, based on the theory of bootstrapping. A series of experiments show that our proposed FCM-DD method can detect drift more accurately, has good sensitivity, and is robust

Concept drift from 1980 to 2020: a comprehensive bibliometric analysis with future research insight

In nonstationary environments, high-dimensional data streams have been generated unceasingly where the underlying distribution of the training and target data may change over time. These drifts are labeled as concept drift in the literature. Learning from evolving data streams demands adaptive or evolving approaches to handle concept drifts, which is a brand-new research affair. In this effort, a wide-ranging comparative analysis of concept drift is represented to highlight state-of-the-art approaches, embracing the last four decades, namely from 1980 to 2020. Considering the scope and discipline; the core collection of the Web of Science database is regarded as the basis of this study, and 1,564 publications related to concept drift are retrieved. As a result of the classification and feature analysis of valid literature data, the bibliometric indicators are revealed at the levels of countries/regions, institutions, and authors. The overall analyses, respecting the publications, citations, and cooperation of networks, are unveiled not only the highly authoritative publications but also the most prolific institutions, influential authors, dynamic networks, etc. Furthermore, deep analyses including text mining such as; the burst detection analysis, co-occurrence analysis, timeline view analysis, and bibliographic coupling analysis are conducted to disclose the current challenges and future research directions. This paper contributes as a remarkable reference for invaluable further research of concept drift, which enlightens the emerging/trend topics, and the possible research directions with several graphs, visualized by using the VOS viewer and Cite Space software