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Review
Computer Science, Artificial Intelligence
Andres L. Suarez-Cetrulo et al.
Summary: This survey reviews the methods and research trends for dealing with concept drift in continuous data streams. It introduces the field of data stream learning, discusses mechanisms for adapting to or detecting concept drifts, and presents supervised and non-supervised methods for handling seasonality in data streams. The aim is to provide future research directions in handling shifts and recurrences in continuous learning scenarios.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Guobo Liao et al.
Summary: This paper proposes a semi-supervised classification approach for evolving data streams, which captures concept drift and concept evolution by maintaining micro-clusters and detecting outliers using local density. Extensive experiments on multiple datasets demonstrate the practicality and effectiveness of the proposed approach for data stream classification.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Jafar Tanha et al.
Summary: This study focuses on semi-supervised data stream classification tasks, particularly limited labeled data and concept drift detection issues. A semi-supervised framework called CPSSDS is proposed, utilizing a self-training framework to handle the scarcity of labeled samples, and employing Conformal Predictors to discover informative unlabeled data instances. The proposed framework shows promising results compared to state-of-the-art algorithms for data streams.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Kanu Goel et al.
Summary: The article presents a new data stream framework DA-DDE, which responds to multiple drift types by combining online and block-based ensemble techniques, demonstrating high effectiveness in handling various drift types.
COMPUTATIONAL INTELLIGENCE
(2022)
Article
Automation & Control Systems
Li Yang et al.
Summary: With the increasing data generation speed in IoT systems, the application of machine learning and automated machine learning techniques has become vital for effective data analytics. This paper provides a review of existing methods in model selection, tuning, and updating procedures in the field of AutoML, aiming to identify optimal solutions for applying ML algorithms to IoT data analytics. A case study is conducted to demonstrate the application of AutoML to IoT anomaly detection, and the challenges and research directions in this domain are discussed and classified.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
S. Priya et al.
Summary: This study analyzes different methods for handling imbalanced data and concept drift, evaluates the performance of various learners on different datasets using performance metrics, and demonstrates that ensemble classifiers perform better than single classifiers when dealing with concept drift and class imbalance data.
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Pawel Zyblewski et al.
Summary: This work focuses on connecting two rarely combined research directions - non-stationary data stream classification and data analysis with skewed class distributions. By proposing a novel framework that employs stratified bagging for training base classifiers and integrating data preprocessing and dynamic ensemble selection methods, the study aims to improve the classification of imbalanced data streams.
INFORMATION FUSION
(2021)
Article
Computer Science, Artificial Intelligence
Husheng Guo et al.
Summary: SEOA is proposed to address the issue of concept drift in streaming data mining, which combines deep learning and ensemble learning methods to improve model convergence and show good robustness and generalization in experiments.
Article
Computer Science, Artificial Intelligence
Weike Liu et al.
Summary: The paper proposed a comprehensive active learning method (CALMID) for handling multiclass imbalanced streaming data with concept drift. Novel uncertainty strategies and sample weight formulas were designed, and experimental results showed that CALMID outperformed existing algorithms in various imbalance and drift scenarios.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Automation & Control Systems
Anjin Liu et al.
Summary: The paper introduces a cluster-based histogram method called EI-kMeans and a heuristic method to improve drift detection sensitivity. By using Pearson's chi-square test for statistical hypothesis testing and dynamically determining the number and shapes of bins, the accuracy of drift detection is enhanced. Experimental results demonstrate the significant advantages of EI-kMeans in detecting concept drift.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Article
Computer Science, Information Systems
Mohammad Noor Injadat et al.
Summary: The paper introduces an optimized machine learning-based network intrusion detection system, which improves performance through comparison of different techniques and hyper-parameter optimization. Experimental results show that the model significantly reduces training sample and feature set size, achieving detection accuracies over 99%.
IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT
(2021)
Proceedings Paper
Computer Science, Information Systems
Li Yang et al.
Summary: With the increase in IoT devices and systems, IoT data analytics techniques are crucial for detecting malicious cyber-attacks and securing IoT systems. However, concept drift issues often occur in IoT data analytics due to the dynamic nature of IoT data streams, leading to model degradation and attack detection failure. Traditional data analytics models are static and cannot adapt to changes in data distribution.
2021 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM)
(2021)
Article
Computer Science, Artificial Intelligence
Christoph Raab et al.
Article
Computer Science, Information Systems
Salah Ud Din et al.
INFORMATION SCIENCES
(2020)
Article
Computer Science, Artificial Intelligence
Li Yang et al.
Article
Computer Science, Artificial Intelligence
Yang Lu et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2020)
Article
Computer Science, Information Systems
Silas G. T. C. Santos et al.
INFORMATION SCIENCES
(2019)
Review
Multidisciplinary Sciences
Scott Wares et al.
SN APPLIED SCIENCES
(2019)
Article
Computer Science, Information Systems
Yu Jiang et al.
Review
Computer Science, Artificial Intelligence
Jie Lu et al.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2019)
Article
Computer Science, Artificial Intelligence
Anjin Liu et al.
PATTERN RECOGNITION
(2018)
Proceedings Paper
Computer Science, Theory & Methods
Zhuo Chen et al.
2018 IEEE INTERNATIONAL CONFERENCE ON BIG DATA AND SMART COMPUTING (BIGCOMP)
(2018)
Article
Computer Science, Artificial Intelligence
Yuhong Zhang et al.
Article
Computer Science, Information Systems
Yange Sun et al.
INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS
(2016)
Article
Computer Science, Artificial Intelligence
Ryan Elwell et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS
(2011)
