Journal Article10.1016/J.INS.2019.08.050
Exploiting evolving micro-clusters for data stream classification with emerging class detection
Salah Ud Din,Junming Shao +1 more
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TL;DR: A new data stream classification approach, called EMC, which dynamically learns a set of online micro-clusters to examine both concept drift and evolution and has good classification and novel class detection performance compared to state-of-the-art algorithms.
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About: This article is published in Information Sciences. The article was published on 01 Jan 2020. The article focuses on the topics: Data stream mining & Concept drift.
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Citations
Research on Mining of Applied Mathematics Educational Resources Based on Edge Computing and Data Stream Classification
Liping Lu,Jing Zhou +1 more
TL;DR: In this article, a resource system architecture suitable for existing applied mathematics education through edge computing technology, which can effectively improve the efficiency of data mining is established, and the data stream classification algorithm is used for information extraction and classification integration of massive applied mathematical education data.
Data stream classification with novel class detection: a review, comparison and challenges
Salah Ud Din,Salah Ud Din,Junming Shao,Jay Kumar,Cobbinah Bernard Mawuli,S. M. Hasan Mahmud,Wei Zhang,Qinli Yang +7 more
TL;DR: A comprehensive overview of the existing works in this line of research can be found in this paper, where the authors discuss and analyze various aspects of the proposed algorithms for data stream classification with concept evolution detection and adaptation.
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Impact of environmental change on runoff in a transitional basin: Tao River Basin from the Tibetan Plateau to the Loess Plateau, China
Long Sun,Yue-Yang Wang,Jianyun Zhang,Qinli Yang,Zhenxin Bao,Xiaoxiang Guan,Tiesheng Guan,Xin Chen,Guoqing Wang +8 more
TL;DR: In this article, the authors quantitatively separated the impacts of climate change and human activities on runoff change in the Tao River by using RCC-WBM model, and found that human activities are the principal drivers of runoff reduction in Tao River Basin.
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Selective prototype-based learning on concept-drifting data streams
TL;DR: Experimental results show that the SPL has better classification performance than many other state-of-the-art algorithms, and has great capabilities to distinguish noise/outliers from drifting instances.
23
Learning multiple gaussian prototypes for open-set recognition
TL;DR: Liu et al. as discussed by the authors learn multiple Gaussian prototypes to better represent the complex classes distribution in both generative and discriminative ways, with the generative constraint, the latent variables of the same class clusters compactly around the corresponding Gaussian prototype, preserving extra space for the samples of unknown classes.
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References
LOF: identifying density-based local outliers
Markus M. Breunig,Hans-Peter Kriegel,Raymond T. Ng,Jörg Sander +3 more
- 16 May 2000
TL;DR: This paper contends that for many scenarios, it is more meaningful to assign to each object a degree of being an outlier, called the local outlier factor (LOF), and gives a detailed formal analysis showing that LOF enjoys many desirable properties.
7.3K
A survey on concept drift adaptation
TL;DR: The survey covers the different facets of concept drift in an integrated way to reflect on the existing scattered state of the art and aims at providing a comprehensive introduction to the concept drift adaptation for researchers, industry analysts, and practitioners.
Mining high-speed data streams
Pedro Domingos,Geoff Hulten +1 more
- 01 Aug 2000
TL;DR: This paper describes and evaluates VFDT, an anytime system that builds decision trees using constant memory and constant time per example, and applies it to mining the continuous stream of Web access data from the whole University of Washington main campus.
Mining time-changing data streams
Geoff Hulten,Laurie Spencer,Pedro Domingos +2 more
- 26 Aug 2001
TL;DR: An efficient algorithm for mining decision trees from continuously-changing data streams, based on the ultra-fast VFDT decision tree learner is proposed, called CVFDT, which stays current while making the most of old data by growing an alternative subtree whenever an old one becomes questionable, and replacing the old with the new when the new becomes more accurate.
•Proceedings Article
Learning from Time-Changing Data with Adaptive Windowing
Albert Bifet,Ricard Gavaldà +1 more
- 01 Jan 2007
TL;DR: A new approach for dealing with distribution change and concept drift when learning from data sequences that may vary with time is presented, using sliding windows whose size is recomputed online according to the rate of change observed from the data in the window itself.