Ensemble framework for concept-drift detection in multidimensional streaming data

Prasad, Kalli Srinivasa Nageswara; Rao, Annaluri Sreenivasa; Ramana, Attili Venkata

doi:10.1080/1206212x.2020.1711617

Cited by 6 publications

(3 citation statements)

References 24 publications

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“…Additionally, the framework can be easily implemented based on the results of the time series decomposition of the dataset used. Experimental assessment of the proposed TBD framework and existing methods EFCDD (5) , OAL Ensemble (6) , CIDD (11) , Meta-ADD (12) and is implemented in Python.…”

Section: Performance Analysis Of Tbd Frameworkmentioning

confidence: 99%

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A Framework to Detect and Classify Time-based Concept Drift

Thangam,

Bhuvaneswari,

Sangeetha

2023

IJST

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Objectives: To design a framework that performs time series decomposition to detect and classify the types of concept drift in a data stream. The aim of this research is to increase the classification accuracy in the detection and classification of drifts. Methods: The proposed method is validated using the Beijing PM2.5 dataset available in the UCI Machine Learning Repository. This dataset has 13 attributes and experiments were performed with the existing drift detection framework algorithms such as EFCDD, Meta-ADD, CIDD, and comparisons were performed with the proposed TBD framework. The outcome of this research is aggregated with Classification accuracy. An effective algorithm selection framework is presented that detects and classifies timebased concept drift existing in the data. The temporal aspects of the data are decomposed to determine the algorithm to be applied to detect and classify the types of drifts. Depending on the decomposed levels, three varied algorithms have been applied and used for the effective detection and classification of time-based drifts. Findings: The performance of the proposed method is validated using the classification accuracy and compared with the existing drift detection framework algorithms. The proposed framework achieves maximum classification accuracy of 95.24% than all the other existing methods. Novelty: A novel framework has been proposed with better classification accuracy for the detection and classification of time-based concept drift.

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Section: Performance Analysis Of Tbd Frameworkmentioning

confidence: 99%

“…Prasad et al (5) proposed a framework to detect the presence of concept drift by using the "Ensemble Framework for Concept Drift Detection (EFCDD)" method. This method evaluates the distribution similarity of data by a scale of measurement known as data variants weight pattern.…”

Section: Introductionmentioning

confidence: 99%

A Framework to Detect and Classify Time-based Concept Drift

Thangam,

Bhuvaneswari,

Sangeetha

2023

IJST

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“…Such issues are particularly prominent in applications deployed over a non-stationary stream where data is continuously provided to the system. Many studies [ 5 , 6 , 7 , 8 , 9 ] in the past decade have focused on concept drift challenges when performing classification over a data stream. However, most of the studies proposed solutions are for non-imaging data streams (which possess low dimensional data).…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Deep Learning Framework for Dynamic Image Classification in the Internet of Things Environment

Jameel

Hashmani

Rehman

et al. 2020

Sensors

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In the modern era of digitization, the analysis in the Internet of Things (IoT) environment demands a brisk amalgamation of domains such as high-dimension (images) data sensing technologies, robust internet connection (4 G or 5 G) and dynamic (adaptive) deep learning approaches. This is required for a broad range of indispensable intelligent applications, like intelligent healthcare systems. Dynamic image classification is one of the major areas of concern for researchers, which may take place during analysis under the IoT environment. Dynamic image classification is associated with several temporal data perturbations (such as novel class arrival and class evolution issue) which cause a massive classification deterioration in the deployed classification models and make them in-effective. Therefore, this study addresses such temporal inconsistencies (novel class arrival and class evolution issue) and proposes an adapted deep learning framework (ameliorated adaptive convolutional neural network (CNN) ensemble framework), which handles novel class arrival and class evaluation issue during dynamic image classification. The proposed framework is an improved version of previous adaptive CNN ensemble with an additional online training (OT) and online classifier update (OCU) modules. An OT module is a clustering-based approach which uses the Euclidean distance and silhouette method to determine the potential new classes, whereas, the OCU updates the weights of the existing instances of the ensemble with newly arrived samples. The proposed framework showed the desirable classification improvement under non-stationary scenarios for the benchmark (CIFAR10) and real (ISIC 2019: Skin disease) data streams. Also, the proposed framework outperformed against state-of-art shallow learning and deep learning models. The results have shown the effectiveness and proven the diversity of the proposed framework to adapt the new concept changes during dynamic image classification. In future work, the authors of this study aim to develop an IoT-enabled adaptive intelligent dermoscopy device (for dermatologists). Therefore, further improvements in classification accuracy (for real dataset) is the future concern of this study.

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Machine learning in concept drift detection using statistical measures

Ali Abdu,

Basulaim

2023

International Journal of Computers and Applications

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Ensemble framework for concept-drift detection in multidimensional streaming data

Cited by 6 publications

References 24 publications

A Framework to Detect and Classify Time-based Concept Drift

A Framework to Detect and Classify Time-based Concept Drift

An Adaptive Deep Learning Framework for Dynamic Image Classification in the Internet of Things Environment

Machine learning in concept drift detection using statistical measures

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