CLASSIFICATION OF CONCEPT DRIFT IN EVOLVING DATA STREAM

2020 ◽  
pp. 189-205
Author(s):  
Mashail Althabiti ◽  
Manal Abdullah
Keyword(s):  
Data Stream ◽  
Concept Drift ◽  
Evolving Data

Decision Tree Classification Algorithm within Concept Similarity

2012 ◽  
Vol 235 ◽  
pp. 9-14
Author(s):  
Chun Hua Ju ◽  
Li Li Mao
Keyword(s):  
Data Streams ◽  
Data Stream ◽  
Concept Drift ◽  
Classification Algorithm ◽  
Streaming Data ◽  
Decision Tree Classification ◽  
The Cost ◽  
Prediction Efficiency ◽  
Concept Similarity

Data stream mining has been applied in many domains, but the concept drifts of data streams bring great obstacles to data mining. Current researches about classification algorithm for streaming data with concept drift have achieved many successes, while they pay little attention to the iterancy of data streams, namely, the situation of the historical concept reappears. For this characteristic, this paper puts forward that it utilizes the classifier model of the historical concepts or high similarity concepts through calculating the concept similarity to classify and predict. In this way, we don’t need training any more. Meanwhile, it reduces the cost of update model, speeds up the classification of the rate and improves the prediction efficiency.

Analyzing and repairing concept drift adaptation in data stream classification

2021 ◽  
Author(s):  
Ben Halstead ◽  
Yun Sing Koh ◽  
Patricia Riddle ◽  
Russel Pears ◽  
Mykola Pechenizkiy ◽  
...  
Keyword(s):  
Data Stream ◽  
Concept Drift ◽  
Stream Classification ◽  
Data Stream Classification

Long short-term memory self-adapting online random forests for evolving data stream regression

2021 ◽  
Author(s):  
Yuan Zhong ◽  
Hongyu Yang ◽  
Yanci Zhang ◽  
Ping Li ◽  
Cheng Ren
Keyword(s):  
Random Forests ◽  
Data Stream ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Evolving Data

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.

Evolving Data Monitoring Algorithm based on Data Stream Clustering

2021 ◽  
Vol 1955 (1) ◽  
pp. 012048
Author(s):  
Chunhua Yang ◽  
Cong Wang ◽  
Xiao Hu ◽  
Niankang You ◽  
Xuguang Yang
Keyword(s):  
Data Stream ◽  
Data Monitoring ◽  
Stream Clustering ◽  
Data Stream Clustering ◽  
Evolving Data ◽  
Monitoring Algorithm

scholarly journals Classification of data stream based on dynamic feature extraction and neural network

2010 ◽  
Vol 30 (6) ◽  
pp. 1539-1542
Author(s):  
Cheng-liang WANG ◽  
Xu PANG ◽  
Zhi-jian LU ◽  
Chang-yin LUO
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Data Stream ◽  
Dynamic Feature
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