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Dynamic classifier selection for effective mining from ...

Request PDF on ResearchGate | Dynamic classifier selection for effective mining from noisy data streams | Mining from data streams has become an important …

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Dynamic Classifier Selection for Effective Mining from ...

305 Dynamic Classifier Selection for Effective Mining from Noisy Data Streams Xingquan Zhu, Xindong Wu, and Ying Yang Department of Computer Science, University of Vermont, Burlington VT 05405, USA

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Proximity Warning Systems for Mining Equipment Workshop

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Dynamic classifier selection for effective mining from ...

In this paper, we propose a new dynamic classifier selection (DCS) mechanism to integrate base classifiers for effective mining from data streams. The proposed algorithm dynamically selects a single "best" classifier to classify each test instance at run time.

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A dynamic rule-induction method for classification in data ...

Parallel and distributed single and multi-label associative classification data mining frameworks based MapReduce. ... The classifier size of PRISM and dynamic rule induction (DRI) algorithms on the data sets. ... Journal of Management Analytics. Volume 2, 2015 - Issue 1. Published online: 19 Mar 2015.

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Mining Multi-label Concept-Drifting Data Streams Using ...

Our empirical study on both synthetic data set and real-life data set shows that the proposed dynamic classifier ensemble with improved binary relevance approach outperforms dynamic classifier ensemble with binary relevance algorithm, and static classifier ensemble with binary relevance algorithm.

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Mining Multi-label Concept-Drifting Data Streams Using ...

Xingquan Zhu, Xindong Wu, Ying Yang, Dynamic Classifier Selection for Effective Mining from Noisy Data Streams, Proceedings of the Fourth IEEE International Conference on Data Mining, p.305-312, November 01-04, 2004

Concept drift - Wikipedia

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