A hybrid block-based ensemble framework for the multi-class problem to react to different types of drifts

Mahdi, Osama A.; Pardede, Eric; Ali, Nawfal

doi:10.1007/s10586-021-03267-7

Cited by 8 publications

(5 citation statements)

References 41 publications

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“…DMDDM-S (Diversity Measure as a new Drift Detection Method for Streaming Data without Class Labels) was designed to detect sudden drifts in the absence of class labels, monitoring the diversity of a pair of classifiers instead of error estimates [36]. HBBE (Hybrid Block-Based Ensemble) is a hybrid block-based ensemble for multi-class classification in evolving data streams, which integrates the strengths of an online drift detector for a k-class problem and the concept of block-based weighting to respond effectively to different types of drifts [37]. EBE (Entropy Based Ensemble) uses information entropy to detect concept drifts, incorporating Measure DT; 4.…”

Section: A Statistical-based Methodsmentioning

confidence: 99%

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Roadmap of Concept Drift Adaptation in Data Stream Mining, Years Later

Mahdi,

Ali,

Pardede

et al. 2024

IEEE Access

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As machine learning models are increasingly applied to real-world scenarios, it is essential to consider the possibility of changes in the data distribution over time. Concept drift detection and adaptation refers to the process of identifying and tracking these changes and updating the model accordingly.Researchers have devoted significant efforts to develop various techniques and tools for concept drift detection and adaptation, as this paper provides a generic roadmap and review of the field. In this paper, we begin by reviewing the background of data stream classification and its assumptions and requirements. Then, we explore the historical development of concept drift detection and adaptation and highlight the key points of approaches that have emerged over time. Next, we summarize the major findings, challenges, and limitations of past research, and provide insights into potential future directions of the field. The paper can benefit researchers and practitioners who seek to navigate the challenges and opportunities in concept drift detection and adaptation.

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Section: A Statistical-based Methodsmentioning

confidence: 99%

“…Abrupt HBBE [37] 2021 • Mechanism: Introduces a hybrid block-based ensemble framework designed for multi-class classification in continuously changing data streams.…”

Section: Abrupt Gradualmentioning

confidence: 99%

Roadmap of Concept Drift Adaptation in Data Stream Mining, Years Later

Mahdi,

Ali,

Pardede

et al. 2024

IEEE Access

Self Cite

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“…Year Drift Type Sudden Gradual Incremental Recurring SSE-PBS [169] 2021 ODKK [161] 2021 RACE [160] 2021 LIR-eGB [184] 2021 CALMID [164] 2021 Nacre [133] 2021 SEDD [114] 2021 OFE-UECM [93] 2020 FDA [112] 2020 ACDDM [101] 2020 HDWM [153] 2020 OS-ELMs [123] 2020 DCS-LA [162] 2020 HLFR [110] 2019 RDWM [154] 2019 CSDD [132] 2019 ECPF [175] 2019 FHDDMS,FHDDMS add [100] 2018 FPDD, FSDD [130] 2018 FTRL-ADP [106] 2018 KME-TEST l [174] 2018 WSTD [128] 2018 MDDM [134] 2018 RDDM [96] 2017 ADDS [142] 2017 AL-ELM [172] 2017 FPH-DD [102] 2016 MOS-ELM [125] 2016 NDE [173] 2016 FHDDM [99] 2016 DOED [159] 2015 HDDM [97] 2015 ESOS-ELM [170] 2015 LFR [109] 2015 DDM-PHT [104] 2015 EDIST [140] 2014 OAUE [150] 2014 SEED [127] 2014 AGE [151] 2014 ACCD [165] 2014 ADDM [136] 2014 AUE2 [150] 2014 LEARN++.CDS [156] 2013 ECDD …”

Section: Methodsmentioning

confidence: 99%

“…Recurrent Adaptive Classifier Ensemble (RACE) [160] preserves an archive of diverse learners and uses EDDM to detect recurring drifts. The online drift detector for the K-class problem (ODDK) [161] was proposed to handle multi-class problems with concept drift. The algorithm constructs a contingency table that stores the variation of the diversity of a pair of classifiers and uses the PH test to detect concept drift.…”

Section: Ensemble Learningmentioning

confidence: 99%

From Concept Drift to Model Degradation: An Overview on Performance-Aware Drift Detectors

Bayram¹,

Ahmed²,

Kassler³

2022

Preprint

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The dynamicity of real-world systems poses a significant challenge to deployed predictive machine learning (ML) models. Changes in the system on which the ML model has been trained may lead to performance degradation during the system's life cycle. Recent advances that study non-stationary environments have mainly focused on identifying and addressing such changes caused by a phenomenon called concept drift. Different terms have been used in the literature to refer to the same type of concept drift and the same term for various types. This lack of unified terminology is set out to create confusion on distinguishing between different concept drift variants. In this paper, we start by grouping concept drift types by their mathematical definitions and survey the different terms used in the literature to build a consolidated taxonomy of the field. We also review and classify performance-based concept drift detection methods proposed in the last decade. These methods utilize the predictive model's performance degradation to signal substantial changes in the systems. The classification is outlined in a hierarchical diagram to provide an orderly navigation between the methods. We present a comprehensive analysis of the main attributes and strategies for tracking and evaluating the model's performance in the

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“…The detection and management of CD in a data stream has been addressed in a variety of ways. A hybrid block-based ensemble was presented in [ 31 ] by means of a framework for multi-class classification in dynamic data streams. In order to respond to various forms of drift, the multi-class framework intends to integrate the key benefits of an online drift detector for a k-class problem and the idea of block-based weighting.…”

Section: Literature Surveymentioning

confidence: 99%

Enhanced Intrusion Detection with Data Stream Classification and Concept Drift Guided by the Incremental Learning Genetic Programming Combiner

Shyaa

Zainol

Abdullah

et al. 2023

Sensors

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Concept drift (CD) in data streaming scenarios such as networking intrusion detection systems (IDS) refers to the change in the statistical distribution of the data over time. There are five principal variants related to CD: incremental, gradual, recurrent, sudden, and blip. Genetic programming combiner (GPC) classification is an effective core candidate for data stream classification for IDS. However, its basic structure relies on the usage of traditional static machine learning models that receive onetime training, limiting its ability to handle CD. To address this issue, we propose an extended variant of the GPC using three main components. First, we replace existing classifiers with alternatives: online sequential extreme learning machine (OSELM), feature adaptive OSELM (FA-OSELM), and knowledge preservation OSELM (KP-OSELM). Second, we add two new components to the GPC, specifically, a data balancing and a classifier update. Third, the coordination between the sub-models produces three novel variants of the GPC: GPC-KOS for KA-OSELM; GPC-FOS for FA-OSELM; and GPC-OS for OSELM. This article presents the first data stream-based classification framework that provides novel strategies for handling CD variants. The experimental results demonstrate that both GPC-KOS and GPC-FOS outperform the traditional GPC and other state-of-the-art methods, and the transfer learning and memory features contribute to the effective handling of most types of CD. Moreover, the application of our incremental variants on real-world datasets (KDD Cup ‘99, CICIDS-2017, CSE-CIC-IDS-2018, and ISCX ‘12) demonstrate improved performance (GPC-FOS in connection with CSE-CIC-IDS-2018 and CICIDS-2017; GPC-KOS in connection with ISCX2012 and KDD Cup ‘99), with maximum accuracy rates of 100% and 98% by GPC-KOS and GPC-FOS, respectively. Additionally, our GPC variants do not show superior performance in handling blip drift.

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A hybrid block-based ensemble framework for the multi-class problem to react to different types of drifts

Cited by 8 publications

References 41 publications

Roadmap of Concept Drift Adaptation in Data Stream Mining, Years Later

Roadmap of Concept Drift Adaptation in Data Stream Mining, Years Later

From Concept Drift to Model Degradation: An Overview on Performance-Aware Drift Detectors

Enhanced Intrusion Detection with Data Stream Classification and Concept Drift Guided by the Incremental Learning Genetic Programming Combiner

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