A Classifier Graph Based Recurring Concept Detection and Prediction Approach

It is common in real-world data streams that previously seen concepts will reappear, which suggests a unique kind of concept drift, known as recurring concepts. Unfortunately, most of existing algorithms do not take full account of this case. Motivated by this challenge, a novel paradigm was proposed for capturing and exploiting recurring concepts in data streams. It not only incorporates a distribution-based change detector for handling concept drift but also captures recurring concept by storing recurring concepts in a classifier graph. The possibility of detecting recurring drifts allows reusing previously learnt models and enhancing the overall learning performance. Extensive experiments on both synthetic and real-world data streams reveal that the approach performs significantly better than the state-of-the-art algorithms, especially when concepts reappear.

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Concept Drift Adaptation Techniques in Distributed Environment for Real-World Data Streams

Smart Cities ◽

10.3390/smartcities4010021 ◽

2021 ◽

Vol 4 (1) ◽

pp. 349-371

Author(s):

Hassan Mehmood ◽

Panos Kostakos ◽

Marta Cortes ◽

Theodoros Anagnostopoulos ◽

Susanna Pirttikangas ◽

...

Keyword(s):

Real World ◽

Data Streams ◽

Smart City ◽

Smart Cities ◽

Concept Drift ◽

Distributed Environment ◽

Real World Data ◽

Unique Challenge ◽

World Data ◽

Concept Drift Detection

Real-world data streams pose a unique challenge to the implementation of machine learning (ML) models and data analysis. A notable problem that has been introduced by the growth of Internet of Things (IoT) deployments across the smart city ecosystem is that the statistical properties of data streams can change over time, resulting in poor prediction performance and ineffective decisions. While concept drift detection methods aim to patch this problem, emerging communication and sensing technologies are generating a massive amount of data, requiring distributed environments to perform computation tasks across smart city administrative domains. In this article, we implement and test a number of state-of-the-art active concept drift detection algorithms for time series analysis within a distributed environment. We use real-world data streams and provide critical analysis of results retrieved. The challenges of implementing concept drift adaptation algorithms, along with their applications in smart cities, are also discussed.

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ADES: A New Ensemble Diversity-Based Approach for Handling Concept Drift

Mobile Information Systems ◽

10.1155/2021/5549300 ◽

2021 ◽

Vol 2021 ◽

pp. 1-17

Author(s):

Tinofirei Museba ◽

Fulufhelo Nelwamondo ◽

Khmaies Ouahada

Keyword(s):

Machine Learning ◽

Real World ◽

Data Streams ◽

Predictive Models ◽

Concept Drift ◽

Dynamic Environments ◽

Real World Data ◽

World Data ◽

Different Types ◽

Concept Drifts

Beyond applying machine learning predictive models to static tasks, a significant corpus of research exists that applies machine learning predictive models to streaming environments that incur concept drift. With the prevalence of streaming real-world applications that are associated with changes in the underlying data distribution, the need for applications that are capable of adapting to evolving and time-varying dynamic environments can be hardly overstated. Dynamic environments are nonstationary and change with time and the target variables to be predicted by the learning algorithm and often evolve with time, a phenomenon known as concept drift. Most work in handling concept drift focuses on updating the prediction model so that it can recover from concept drift while little effort has been dedicated to the formulation of a learning system that is capable of learning different types of drifting concepts at any time with minimum overheads. This work proposes a novel and evolving data stream classifier called Adaptive Diversified Ensemble Selection Classifier (ADES) that significantly optimizes adaptation to different types of concept drifts at any time and improves convergence to new concepts by exploiting different amounts of ensemble diversity. The ADES algorithm generates diverse base classifiers, thereby optimizing the margin distribution to exploit ensemble diversity to formulate an ensemble classifier that generalizes well to unseen instances and provides fast recovery from different types of concept drift. Empirical experiments conducted on both artificial and real-world data streams demonstrate that ADES can adapt to different types of drifts at any given time. The prediction performance of ADES is compared to three other ensemble classifiers designed to handle concept drift using both artificial and real-world data streams. The comparative evaluation performed demonstrated the ability of ADES to handle different types of concept drifts. The experimental results, including statistical test results, indicate comparable performances with other algorithms designed to handle concept drift and prove their significance and effectiveness.

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Concept Drift Detection in Dynamic Probabilistic Relational Models

The International FLAIRS Conference Proceedings ◽

10.32473/flairs.v34i1.128465 ◽

2021 ◽

Vol 34 (1) ◽

Author(s):

Nils Finke ◽

Tanya Braun ◽

Marcel Gehrke ◽

Ralf Möller

Keyword(s):

Real World ◽

Joint Distribution ◽

Concept Drift ◽

Probability Distributions ◽

Real World Data ◽

Relational Models ◽

World Data ◽

Probabilistic Relational Models ◽

Changes Over Time ◽

Over Time

Dynamic probabilistic relational models, which are factorized w.r.t. a full joint distribution, are used to cater for uncertainty and for relational and temporal aspects in real-world data. While these models assume the underlying temporal process to be stationary, real-world data often exhibits non-stationary behavior where the full joint distribution changes over time. We propose an approach to account for non-stationary processes w.r.t. to changing probability distributions over time, an effect known as concept drift. We use factorization and compact encoding of relations to efficiently detect drifts towards new probability distributions based on evidence.

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Show Me What You’re Looking For

The International FLAIRS Conference Proceedings ◽

10.32473/flairs.v34i1.128399 ◽

2021 ◽

Vol 34 (1) ◽

Author(s):

Leonid Schwenke ◽

Martin Atzmueller

Keyword(s):

Time Series ◽

Real World ◽

Specific Form ◽

Learning Performance ◽

Complex Data ◽

Real World Data ◽

World Data

While Transformers have shown their advantages consideringtheir learning performance, their lack of explainabilityand interpretability is still a major problem.This specifically relates to the processing of time series,as a specific form of complex data. In this paper,we propose an approach for visualizing abstracted informationin order to enable computational sensemakingand local interpretability on the respective Transformermodel. Our results demonstrate the efficacy ofthe proposed abstraction method and visualization, utilizingboth synthetic and real world data for evaluation.

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Recurrent Adaptive Classifier Ensemble for Handling Recurring Concept Drifts

Applied Computational Intelligence and Soft Computing ◽

10.1155/2021/5533777 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Tinofirei Museba ◽

Fulufhelo Nelwamondo ◽

Khmaies Ouahada ◽

Ayokunle Akinola

Keyword(s):

Machine Learning ◽

Real World ◽

Concept Drift ◽

Learning Algorithms ◽

Learning Model ◽

Machine Learning Algorithms ◽

Classifier Ensemble ◽

Series Data ◽

Real World Data ◽

World Data

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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