Passive concept drift handling via variations of learning vector quantization

Heusinger, Moritz; Raab, Christoph; Schleif, Frank-Michael

doi:10.1007/s00521-020-05242-6

Cited by 11 publications

(6 citation statements)

References 25 publications

(45 reference statements)

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“…Lastly, Table 3 emphasises the overall number of publications that were found, evaluated, and ultimately determined to be appropriate for inclusion in this SLR research. The existing approaches which deal with concept drift are generally divided into two type types: Active approach and Passive approach (Heusinger et al, 2022). The active approach uses an explicit drift The existing approaches which deal with concept drift are generally divided into two type types:…”

Section: Inclusion and Exclusion Criteriamentioning

confidence: 99%

Online Machine Learning from Non-stationary Data Streams in the Presence of Concept Drift and Class Imbalance: A Systematic Review

Palli,

Jaafar,

Gilal

et al. 2024

JICT

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In IoT environment applications generate continuous non-stationary data streams with in-built problems of concept drift and class imbalance which cause classifier performance degradation. The imbalanced data affects the classifier during concept detection and concept adaptation. In general, for concept detection, a separate mechanism is added in parallel with the classifier to detect the concept drift called a drift detector. For concept adaptation, the classifier updates itself or trains a new classifier to replace the older one. In case, the data stream faces a class imbalance issue, the classifier may not properly adapt to the latest concept. In this survey, we study how the existing work addresses the issues of class imbalance and concept drift while learning from nonstationarydata streams. We further highlight the limitation of existing work and challenges caused by other factors of class imbalance alongwith concept drift in data stream classification. Results of our survey found that, out of 1110 studies, by using our inclusion and exclusion criteria, we were able to narrow the pool of articles down to 35 that directly addressed our study objectives. The study found that issues such as multiple concept drift types, dynamic class imbalance ratio, and multi-class imbalance in presence of concept drift are still open for further research. We also observed that, while major research efforts have been dedicated to resolving concept drift and class imbalance, not much attention has been given to with-in-class imbalance, rear examples, and borderline instances when they exist with concept drift in multi-class data. This paper concludes with some suggested future directions.

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Section: Inclusion and Exclusion Criteriamentioning

confidence: 99%

Online Machine Learning from Non-stationary Data Streams in the Presence of Concept Drift and Class Imbalance: A Systematic Review

Palli,

Jaafar,

Gilal

et al. 2024

JICT

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“…Concept drift adaptation is categorized into two approaches: active adaptation [10] and passive adaptation [8]. Active adaptation involves proactive detection upon data arrival to identify and update the model for new concepts.…”

Section: Active Passive Adaptation For Concept Driftmentioning

confidence: 99%

“…generate feature vectors z i and z i in RKHS, and d(z i , c i−1 ) by Equation (8). compute d min , d max by Equation (9).…”

Section: Learning Online Based On Stream Datamentioning

confidence: 99%

Scalable concept drift adaptation for stream data mining

Hu,

Li,

et al. 2024

Complex Intell. Syst.

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Stream data mining aims to handle the continuous and ongoing generation of data flows (e.g. weather, stock and traffic data), which often encounters concept drift as time progresses. Traditional offline algorithms struggle with learning from real-time data, making online algorithms more fitting for mining the stream data with dynamic concepts. Among families of the online learning algorithms, single pass stands out for its efficiency in processing one sample point at a time, and inspecting it only once at most. Currently, there exist online algorithms tailored for single pass over the stream data by converting the problems of classification into minimum enclosing ball. However, these methods mainly focus on expanding the ball to enclose the new data. An excessively large ball might overwrite data of the new concept, creating difficulty in triggering the model updating process. This paper proposes a new online single pass framework for stream data mining, namely Scalable Concept Drift Adaptation (SCDA), and presents three distinct online methods (SCDA-I, SCDA-II and SCDA-III) based on that framework. These methods dynamically adjust the ball by expanding or contracting when new sample points arrive, thereby effectively avoiding the issue of excessively large balls. To evaluate their performance, we conduct the experiments on 7 synthetic and 5 real-world benchmark datasets and compete with the state-of-the-arts. The experiments demonstrate the applicability and flexibility of the SCDA methods in stream data mining by comparing three aspects: predictive performance, memory usage and scalability of the ball. Among them, the SCDA-III method performs best in all these aspects.

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“…e algorithm weights classifiers according to their performance, and poorly performing classifiers are discarded. Heusinger [9] proposed a combination of the modified versions of Robust Soft Learning Vector Quantization (RSLVQ) and Generalized Learning Vector Quantization (GLVQ) to learn streaming data and adapt to all types of concept drift. e integration of Adadelta and Adamax into RSLVQ and GLVQ optimized the prediction performance over their vanilla versions.…”

Section: Related Workmentioning

confidence: 99%

Recurrent Adaptive Classifier Ensemble for Handling Recurring Concept Drifts

Museba

Nelwamondo

Ouahada

et al. 2021

Applied Computational Intelligence and Soft Computing

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For most real-world data streams, the concept about which data is obtained may shift from time to time, a phenomenon known as concept drift. For most real-world applications such as nonstationary time-series data, concept drift often occurs in a cyclic fashion, and previously seen concepts will reappear, which supports a unique kind of concept drift known as recurring concepts. A cyclically drifting concept exhibits a tendency to return to previously visited states. Existing machine learning algorithms handle recurring concepts by retraining a learning model if concept is detected, leading to the loss of information if the concept was well learned by the learning model, and the concept will recur again in the next learning phase. A common remedy for most machine learning algorithms is to retain and reuse previously learned models, but the process is time-consuming and computationally prohibitive in nonstationary environments to appropriately select any optimal ensemble classifier capable of accurately adapting to recurring concepts. To learn streaming data, fast and accurate machine learning algorithms are needed for time-dependent applications. Most of the existing algorithms designed to handle concept drift do not take into account the presence of recurring concept drift. To accurately and efficiently handle recurring concepts with minimum computational overheads, we propose a novel and evolving ensemble method called Recurrent Adaptive Classifier Ensemble (RACE). The algorithm preserves an archive of previously learned models that are diverse and always trains both new and existing classifiers. The empirical experiments conducted on synthetic and real-world data stream benchmarks show that RACE significantly adapts to recurring concepts more accurately than some state-of-the-art ensemble classifiers based on classifier reuse.

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Passive concept drift handling via variations of learning vector quantization

Cited by 11 publications

References 25 publications

Online Machine Learning from Non-stationary Data Streams in the Presence of Concept Drift and Class Imbalance: A Systematic Review

Online Machine Learning from Non-stationary Data Streams in the Presence of Concept Drift and Class Imbalance: A Systematic Review

Scalable concept drift adaptation for stream data mining

Recurrent Adaptive Classifier Ensemble for Handling Recurring Concept Drifts

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