scholarly journals Concept drift detection in business process logs using deep learning

2021 ◽  
Vol 17 (4) ◽  
pp. 33-48
Author(s):  
fatemeh khojasteh ◽  
Mohsen Kahani ◽  
Behashid Behkamal ◽  
◽  
◽  
...  
Keyword(s):  
Deep Learning ◽  
Business Process ◽  
Concept Drift ◽  
Concept Drift Detection

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.

Process Duration Modelling and Concept Drift Detection for Business Process Mining

2021 ◽  
Author(s):  
Lingkai Yang ◽  
Sally McClean ◽  
Mark Donnelly ◽  
Kevin Burke ◽  
Kashaf Khan
Keyword(s):  
Business Process ◽  
Process Mining ◽  
Concept Drift ◽  
Process Duration ◽  
Concept Drift Detection ◽  
Business Process Mining

scholarly journals An Adaptive Behavioral-Based Incremental Batch Learning Malware Variants Detection Model using Concept Drift Detection and Sequential Deep Learning

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Abdulbasit A. Darem ◽  
Fuad A. Ghaleb ◽  
Asma A. Al-Hashmi ◽  
Jemal H. Abawajy ◽  
Sultan M. Alanazi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Concept Drift ◽  
Detection Model ◽  
Batch Learning ◽  
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 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

Explaining Concept Drift of Deep Learning Models

2019 ◽  
pp. 524-534 ◽  
Author(s):  
Xiaolu Wang ◽  
Zhi Wang ◽  
Wei Shao ◽  
Chunfu Jia ◽  
Xiang Li
Keyword(s):  
Deep Learning ◽  
Concept Drift ◽  
Learning Models
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