Streaming Data Classification using Hybrid Classifiers to tackle Stability-Plasticity Dilemma and Concept Drift

2020 ◽  
Author(s):  
A L Amutha ◽  
R Annie Uthra ◽  
J Preetha Roselyn ◽  
R Golda Brunet
Keyword(s):  
Concept Drift ◽  
Data Classification ◽  
Streaming Data ◽  
Hybrid Classifiers

Streaming Data Classification With Concept Drift

2019 ◽  
Vol 12 (1) ◽  
pp. 177-184
Author(s):  
Mashail Althabiti ◽  
Manal Abdullah*
Keyword(s):  
Concept Drift ◽  
Data Classification ◽  
Streaming Data

scholarly journals Handling adversarial concept drift in streaming data

2018 ◽  
Vol 97 ◽  
pp. 18-40 ◽  
Author(s):  
Tegjyot Singh Sethi ◽  
Mehmed Kantardzic
Keyword(s):  
Concept Drift ◽  
Streaming 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.

An iterative boosting-based ensemble for streaming data classification

2019 ◽  
Vol 45 ◽  
pp. 66-78 ◽  
Author(s):  
João Roberto Bertini Junior ◽  
Maria do Carmo Nicoletti
Keyword(s):  
Data Classification ◽  
Streaming Data

Concept Drift Detection on Streaming Data with Dynamic Outlier Aggregation

2021 ◽  
pp. 206-217
Author(s):  
Ludwig Zellner ◽  
Florian Richter ◽  
Janina Sontheim ◽  
Andrea Maldonado ◽  
Thomas Seidl
Keyword(s):  
Concept Drift ◽  
Streaming Data ◽  
Concept Drift Detection

scholarly journals Data-driven decision support under concept drift in streamed big data

2019 ◽  
Vol 6 (1) ◽  
pp. 157-163 ◽  
Author(s):  
Jie Lu ◽  
Anjin Liu ◽  
Yiliao Song ◽  
Guangquan Zhang
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Real Time ◽  
Concept Drift ◽  
High Volume ◽  
Streaming Data ◽  
Data Driven ◽  
Research Directions ◽  
Decision Outcomes ◽  
Past Data

Abstract Data-driven decision-making ($$\mathrm {D^3}$$D3M) is often confronted by the problem of uncertainty or unknown dynamics in streaming data. To provide real-time accurate decision solutions, the systems have to promptly address changes in data distribution in streaming data—a phenomenon known as concept drift. Past data patterns may not be relevant to new data when a data stream experiences significant drift, thus to continue using models based on past data will lead to poor prediction and poor decision outcomes. This position paper discusses the basic framework and prevailing techniques in streaming type big data and concept drift for $$\mathrm {D^3}$$D3M. The study first establishes a technical framework for real-time $$\mathrm {D^3}$$D3M under concept drift and details the characteristics of high-volume streaming data. The main methodologies and approaches for detecting concept drift and supporting $$\mathrm {D^3}$$D3M are highlighted and presented. Lastly, further research directions, related methods and procedures for using streaming data to support decision-making in concept drift environments are identified. We hope the observations in this paper could support researchers and professionals to better understand the fundamentals and research directions of $$\mathrm {D^3}$$D3M in streamed big data environments.

scholarly journals Learning from streaming data with concept drift and imbalance: an overview

2012 ◽  
Vol 1 (1) ◽  
pp. 89-101 ◽  
Author(s):  
T. Ryan Hoens ◽  
Robi Polikar ◽  
Nitesh V. Chawla
Keyword(s):  
Concept Drift ◽  
Streaming Data
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