Evaluation of early student performance prediction given concept drift by Benedikt Sonnleitner, Tom Madou, Matthias Deceuninck, Filotas Theodosiou, Yves R. Sagaert

“…However, this complexity can lead to inaccurate predictions when concept drift occurs, or when a large number of features are used with a limited sample size. …”
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Towards Supercomputing Categorizing the Maliciousness upon Cybersecurity Blacklists with Concept Drift by M. V. Carriegos, N. DeCastro-García, D. Escudero

“…This situation is known as concept drift. For this reason, it is necessary to study if the optimization proposed works in a concept drift scenario. …”
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Enhancing Semi-Supervised Learning With Concept Drift Detection and Self-Training: A Study on Classifier Diversity and Performance by Jose L. M. Perez, Roberto S. M. Barros, Silas G. T. C. Santos

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Adaptive Oversampling via Density Estimation for Online Imbalanced Classification by Daeun Lee, Hyunjoong Kim

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Machine Learning for Financial Prediction Under Regime Change Using Technical Analysis: A Systematic Review by Andrés L. Suárez-Cetrulo, David Quintana, Alejandro Cervantes

Subjects: “…concept drift…”
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An Adaptive Scalable Data Pipeline for Multiclass Attack Classification in Large-Scale IoT Networks by Selvam Saravanan, Uma Maheswari Balasubramanian

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End-to-End Methodology for Predictive Maintenance Based on Fingerprint Routines and Anomaly Detection for Machine Tool Rotary Components by Amaia Arregi, Aitor Barrutia, Iñigo Bediaga

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A Multi-Granularity Features Representation and Dimensionality Reduction Network for Website Fingerprinting by Yaojun Ding, Bingxuan Hu

“…This degradation results from the evolution of network protocol versions and the ongoing development of obfuscation techniques, a phenomenon known as concept drift. To address the problem of concept drift, this paper presents a multi-granularity features representation and dimensionality reduction network for Website Fingerprinting, referred to as LRCT. …”
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Microcluster-Based Incremental Ensemble Learning for Noisy, Nonstationary Data Streams by Sanmin Liu, Shan Xue, Fanzhen Liu, Jieren Cheng, Xiulai Li, Chao Kong, Jia Wu

“…However, under the environment of concept drift and noise, the research of data stream classification faces lots of challenges. …”
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Online Incremental Learning for High Bandwidth Network Traffic Classification by H. R. Loo, S. B. Joseph, M. N. Marsono

“…Two distance measures for incremental k-means (Euclidean and Manhattan) distance are analyzed to measure their impact on the network traffic classification in the presence of concept drift. The experimental results on real datasets show that the proposed algorithm exhibits consistency, up to 94% average accuracy for both distance measures, even in the presence of concept drifts. …”
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Online Coregularization for Multiview Semisupervised Learning by Boliang Sun, Guohui Li, Li Jia, Kuihua Huang

“…Specially, our online coregularization algorithms are able to deal with concept drift and maintain a much smaller error rate. …”
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Adaptive CNN Ensemble for Complex Multispectral Image Analysis by Syed Muslim Jameel, Manzoor Ahmed Hashmani, Mobashar Rehman, Arif Budiman

“…To highlight this critical issue, firstly, this study formulates the problem of new spectral band arrival as virtual concept drift. Secondly, an adaptive convolutional neural network (CNN) ensemble framework is proposed and evaluated for a new spectral band adaptation. …”
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FABLDroid: Malware detection based on hybrid analysis with factor analysis and broad learning methods for android applications by Kazım Kılıç, İsmail Atacak, İbrahim Alper Doğru

“…The Kronodroid dataset is a dataset consisting of malware and benign applications, specifically designed to examine and explore the concept drift and cross-device detection issues in the problem domain. …”
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