NDLI: Self-adaptive and local strategies for a smooth treatment of drifts in data streams

Please wait, while we are loading the content...

Controlling maximum evaluation duration in on-line and on-board evolutionary robotics : On-the-fly control of maximum evaluation duration in on-line and on-board evolutionary robotics in highly dynamic and uncertain environments

Evolving Systems : Volume 5, Issue 3, September 2014

Evolving Systems : Volume 5, Issue 2, June 2014

Evolving Systems : Volume 5, Issue 1, March 2014

Evolving Systems : Volume 4

Evolving Systems : Volume 3

Evolving Systems : Volume 2

Evolving Systems : Volume 1

Self-adaptive and local strategies for a smooth treatment of drifts in data streams

Content Provider	Springer Nature Link
Author	Shaker, Ammar Lughofer, Edwin
Copyright Year	2014
Abstract	In this paper, we are dealing with a new concept for handling drifts in data streams during the run of on-line, evolving modeling processes in a regression context. Drifts require a specific attention in evolving modeling methods, as they usually change the underlying data distribution making previously learnt model parameters and structure outdated. Our approach comes with three new stages for an appropriate drift handling: (1) drifts are not only detected, but also quantified with a new extended version of the Page-Hinkley test; (2) we integrate an adaptive forgetting factor changing over time and which steers the degree of forgetting in dependency of the current drift intensity in the data stream; (3) we introduce local forgetting factors by addressing the different local regions of the feature space with a different forgetting intensity; this is achieved by using fuzzy model architecture within stream learning whose structural components (fuzzy rules) provide a local partitioning of the feature space and furthermore ensure smooth transitions of drift handling topology between neighboring regions. Additionally, our approach foresees an early drift recognition variant, which relies on divergence measures, indicating the degree of divergence in local parts of the feature space separately already before the global model error may start to rise significantly. Thus, it can be seen as an attempt regarding drift prevention on global model level. The new approach is successfully evaluated and compared with fixed forgetting and no forgetting on high-dimensional real-world data streams, including different types of drifts.
Starting Page	239
Ending Page	257
Page Count	19
File Format	PDF
ISSN	18686478
Journal	Evolving Systems
Volume Number	5
Issue Number	4
e-ISSN	18686486
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2014-05-01
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Data stream learning Adaptive local and global drift handling Drift and forgetting intensity Component divergence Early drift recognition Evolving granular models Complexity Artificial Intelligence (incl. Robotics) Statistical Physics, Dynamical Systems and Complexity
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Control and Systems Engineering Modeling and Simulation Computer Science Applications

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Evolving fuzzy granular modeling from nonstationary fuzzy data streams

An online ensembles approach for handling concept drift in data streams: diversified online ensembles detection

Visualization of evolving fuzzy rule-based systems

Adaptive learning of an evolving cascade neo-fuzzy system in data stream mining tasks

Recursive least square perceptron model for non-stationary and imbalanced data stream classification

One-class classifiers with incremental learning and forgetting for data streams with concept drift

Adaptive evolving strategy for dextrous robotic manipulation

Online self-evolving fuzzy controller with global learning capabilities

Self-Adaptive Windowing Approach for Handling Complex Concept Drift

Self-adaptive and local strategies for a smooth treatment of drifts in data streams

Similar Documents

Evolving fuzzy granular modeling from nonstationary fuzzy data streams

An online ensembles approach for handling concept drift in data streams: diversified online ensembles detection

Visualization of evolving fuzzy rule-based systems

Adaptive learning of an evolving cascade neo-fuzzy system in data stream mining tasks

Recursive least square perceptron model for non-stationary and imbalanced data stream classification

One-class classifiers with incremental learning and forgetting for data streams with concept drift

Adaptive evolving strategy for dextrous robotic manipulation

Online self-evolving fuzzy controller with global learning capabilities

Self-Adaptive Windowing Approach for Handling Complex Concept Drift

Self-adaptive and local strategies for a smooth treatment of drifts in data streams