scholarly journals Adaptive concept drift detection

2009 ◽  
Vol 2 (5-6) ◽  
pp. 311-327 ◽  
Author(s):  
Anton Dries ◽  
Ulrich Rückert
Keyword(s):  
Concept Drift ◽  
Concept Drift Detection

Concept drift detection and localization in process mining

2021 ◽  
Author(s):  
Rafael Gaspar de Sousa ◽  
Sarajane Marques Peres ◽  
Marcelo Fantinato ◽  
Hajo Alexander Reijers
Keyword(s):  
Process Mining ◽  
Concept Drift ◽  
Concept Drift Detection ◽  
Detection And Localization

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

Smart Cities ◽  
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.

scholarly journals Deep learning framework for handling concept drift and class imbalanced complex decision-making on streaming data

2021 ◽  
Author(s):  
S. Priya ◽  
R. Annie Uthra
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Concept Drift ◽  
Class Imbalance ◽  
Streaming Data ◽  
Superior Performance ◽  
Data Streaming ◽  
Minority Class ◽  
Concept Drift Detection

AbstractIn present times, data science become popular to support and improve decision-making process. Due to the accessibility of a wide application perspective of data streaming, class imbalance and concept drifting become crucial learning problems. The advent of deep learning (DL) models finds useful for the classification of concept drift in data streaming applications. This paper presents an effective class imbalance with concept drift detection (CIDD) using Adadelta optimizer-based deep neural networks (ADODNN), named CIDD-ADODNN model for the classification of highly imbalanced streaming data. The presented model involves four processes namely preprocessing, class imbalance handling, concept drift detection, and classification. The proposed model uses adaptive synthetic (ADASYN) technique for handling class imbalance data, which utilizes a weighted distribution for diverse minority class examples based on the level of difficulty in learning. Next, a drift detection technique called adaptive sliding window (ADWIN) is employed to detect the existence of the concept drift. Besides, ADODNN model is utilized for the classification processes. For increasing the classifier performance of the DNN model, ADO-based hyperparameter tuning process takes place to determine the optimal parameters of the DNN model. The performance of the presented model is evaluated using three streaming datasets namely intrusion detection (NSL KDDCup) dataset, Spam dataset, and Chess dataset. A detailed comparative results analysis takes place and the simulation results verified the superior performance of the presented model by obtaining a maximum accuracy of 0.9592, 0.9320, and 0.7646 on the applied KDDCup, Spam, and Chess dataset, respectively.

scholarly journals Enhash: A Fast Streaming Algorithm For Concept Drift Detection

2021 ◽  
Author(s):  
Aashi Jindal ◽  
Prashant Gupta ◽  
Debarka Sengupta ◽  
Jayadeva Jayadeva
Keyword(s):  
Concept Drift ◽  
Streaming Algorithm ◽  
Concept Drift Detection

scholarly journals Concept Drift Detection with Variable Interaction Networks

2020 ◽  
pp. 296-303
Author(s):  
Jan Zenisek ◽  
Gabriel Kronberger ◽  
Josef Wolfartsberger ◽  
Norbert Wild ◽  
Michael Affenzeller
Keyword(s):  
Concept Drift ◽  
Interaction Networks ◽  
Concept Drift Detection ◽  
Variable Interaction

Heuristic ensemble for unsupervised detection of multiple types of concept drift in data stream classification

2021 ◽  
pp. 1-14
Author(s):  
Hanqing Hu ◽  
Mehmed Kantardzic
Keyword(s):  
Data Stream ◽  
Concept Drift ◽  
False Alarms ◽  
Detection Accuracy ◽  
Real World Data ◽  
Traditional Concept ◽  
Stream Classification ◽  
Data Stream Classification ◽  
Detection Algorithms ◽  
Concept Drift Detection

Real-world data stream classification often deals with multiple types of concept drift, categorized by change characteristics such as speed, distribution, and severity. When labels are unavailable, traditional concept drift detection algorithms, used in stream classification frameworks, are often focused on only one type of concept drift. To overcome the limitations of traditional detection algorithms, this study proposed a Heuristic Ensemble Framework for Drift Detection (HEFDD). HEFDD aims to detect all types of concept drift by employing an ensemble of selected concept drift detection algorithms, each capable of detecting at least one type of concept drift. Experimental results show HEFDD provides significant improvement based on the z-score test when comparing detection accuracy with state-of-the-art individual algorithms. At the same time, HEFDD is able to reduce false alarms generated by individual concept drift detection algorithms.

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