Bhattacharyya Distance based Concept Drift Detection Method For evolving data stream

2021 ◽  
pp. 115303
Author(s):  
Ishwar Baidari ◽  
Nagaraj Honnikoll
Keyword(s):  
Data Stream ◽  
Detection Method ◽  
Concept Drift ◽  
Bhattacharyya Distance ◽  
Concept Drift Detection ◽  
Evolving Data

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.

A Novel Concept Drift Detection Method for Incremental Learning in Nonstationary Environments

2020 ◽  
Vol 31 (1) ◽  
pp. 309-320 ◽  
Author(s):  
Zhe Yang ◽  
Sameer Al-Dahidi ◽  
Piero Baraldi ◽  
Enrico Zio ◽  
Lorenzo Montelatici
Keyword(s):  
Incremental Learning ◽  
Detection Method ◽  
Concept Drift ◽  
Concept Drift Detection ◽  
Novel Concept

Applying Fourier Inspired Windows for Concept Drift Detection in Data Stream

2020 ◽  
Author(s):  
Sumit Misra ◽  
Dipan Biswas ◽  
Sanjoy Kumar Saha ◽  
Chandan Mazumdar
Keyword(s):  
Data Stream ◽  
Concept Drift ◽  
Concept Drift Detection

A novel concept drift detection method in data streams using ensemble classifiers

2016 ◽  
Vol 20 (6) ◽  
pp. 1329-1350 ◽  
Author(s):  
Mahdie Dehghan ◽  
Hamid Beigy ◽  
Poorya ZareMoodi
Keyword(s):  
Data Streams ◽  
Detection Method ◽  
Concept Drift ◽  
Ensemble Classifiers ◽  
Concept Drift Detection ◽  
Novel Concept

scholarly journals Data stream mining: methods and challenges for handling concept drift

2019 ◽  
Vol 1 (11) ◽  
Author(s):  
Scott Wares ◽  
John Isaacs ◽  
Eyad Elyan
Keyword(s):  
Data Stream ◽  
Concept Drift ◽  
Relevant Literature ◽  
Streaming Data ◽  
Future Research ◽  
Stream Mining ◽  
Detection Algorithms ◽  
The Past ◽  
Concept Drift Detection ◽  
The Impact

Abstract Mining and analysing streaming data is crucial for many applications, and this area of research has gained extensive attention over the past decade. However, there are several inherent problems that continue to challenge the hardware and the state-of-the art algorithmic solutions. Examples of such problems include the unbound size, varying speed and unknown data characteristics of arriving instances from a data stream. The aim of this research is to portray key challenges faced by algorithmic solutions for stream mining, particularly focusing on the prevalent issue of concept drift. A comprehensive discussion of concept drift and its inherent data challenges in the context of stream mining is presented, as is a critical, in-depth review of relevant literature. Current issues with the evaluative procedure for concept drift detectors is also explored, highlighting problems such as a lack of established base datasets and the impact of temporal dependence on concept drift detection. By exposing gaps in the current literature, this study suggests recommendations for future research which should aid in the progression of stream mining and concept drift detection algorithms.

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