Accuracy weighted diversity-based online boosting

Baidari, Ishwar; Honnikoll, Nagaraj

doi:10.1016/j.eswa.2020.113723

Cited by 11 publications

(4 citation statements)

References 26 publications

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“…DIE assigns weights to classifiers and discards classifiers whose weight is below a predefined threshold, making it difficult to react to recurring concepts. Baidari [14] proposed the Accuracy Weighted Diversity based Online Boosting (AWDOB) which is based on an Adaptable Diversity based Online Boosting (ADOB). AWDOB uses an accuracy weighting scheme that exploits the accuracy of the current expert and the number of correctly classified and incorrectly classified instances of all experts to assign the current expert weight to the current instance in the data stream.…”

Section: Related Workmentioning

confidence: 99%

“…e generalization performance of RACE is compared to other state-of-the-art algorithms designed to handle recurring concepts such as the comprehensive online active learning framework (CALMID) [17], Dynamic Updated Ensemble (DUE) [16], Self-Organizing Fuzzy Ensemble Inference System (SOFEnsemble) [15], and Accuracy Weighted Diversity based Online Boosting (AWDOB) [14].…”

Section: Experimental Configurationmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

Section: Experimental Configurationmentioning

confidence: 99%

Recurrent Adaptive Classifier Ensemble for Handling Recurring Concept Drifts

Museba

Nelwamondo

Ouahada

et al. 2021

Applied Computational Intelligence and Soft Computing

View full text Add to dashboard Cite

show abstract

“…e algorithm does not consider the importance of ensemble diversity, which is a cornerstone to the success of every ensemble classifier. Accuracy Weighted Diversity-based Online Boosting [8] is based on the concept of Diversity-Adaptation-based Online Boosting (ADOB) and other modifications. e algorithm introduces a new way of computing the weights of recently generated classifiers and calculates the sums of correctly and incorrectly classified instances by all classifiers.…”

Section: Related Workmentioning

confidence: 99%

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

Museba

Nelwamondo

Ouahada

2021

Mobile Information Systems

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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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“…Santos et al [55] proposed Adaptable Diversity-based Online Boosting (adob), which can accelerate the update of base classifiers after concept drifts by making the λ parameter dependent on the prediction quality of base classifiers. Based on this approach, Accuracy Weighted Diversity-based Online Boosting (awdob) [4] employs weighted voting according to previous base classifier evaluations results. Baros et al developed Boosting-like Online Learning Ensemble (bole) [5], which used a modification of the adob algorithm involving weakening the requirements to allow the base classifier to vote and use different concept drift detector.…”

Section: Classifier Ensemble For Non-stationary Data Streammentioning

confidence: 99%

Active Weighted Aging Ensemble for Drifted Data Stream Classification

Woźniak¹,

Zyblewski²,

Ksieniewicz³

2021

Preprint

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One of the significant problems of streaming data classification is the occurrence of concept drift, consisting of the change of probabilistic characteristics of the classification task. This phenomenon destabilizes the performance of the classification model and seriously degrades its quality. An appropriate strategy counteracting this phenomenon is required to adapt the classifier to the changing probabilistic characteristics. One of the significant problems in implementing such a solution is the access to data labels. It is usually costly, so to minimize the expenses related to this process, learning strategies based on semisupervised learning are proposed, e.g., employing active learning methods indicating which of the incoming objects are valuable to be labeled for improving the classifier's performance. This paper proposes a novel chunk-based method for non-stationary data streams based on classifier ensemble learning and an active learning strategy considering a limited budget that can be successfully applied to any data stream classification algorithm. The proposed method has been evaluated through computer experiments using both real and generated data streams. The results confirm the high quality of the proposed algorithm over state-of-the-art methods.

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Accuracy weighted diversity-based online boosting

Cited by 11 publications

References 26 publications

Recurrent Adaptive Classifier Ensemble for Handling Recurring Concept Drifts

Recurrent Adaptive Classifier Ensemble for Handling Recurring Concept Drifts

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

Active Weighted Aging Ensemble for Drifted Data Stream Classification

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