期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 117, 期 -, 页码 90-102出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2018.08.054
关键词
Data streams; Concept drift; Algorithmic stability; McDiarmid's inequality
类别
资金
- CoordenaAo de AperfeiAoamento de Pessoal de Ndvel Superior - Coordination for the Improvement of Higher Level Personnel (grants PROEX) [5881819/D, 302077/2017-0]
- FundaAo Amazynia Paraense de Amparo Pesquisa - Sao Paulo Research Foundation [2017/16548-6]
- Conselho Nacional de Desenvolvimento Cientdfico e Tecnolgico - The Brazilian National Council for Scientific and Technological Development [302077/2017-0]
- Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP) [17/16548-6] Funding Source: FAPESP
Motivated by the Statistical Learning Theory (SLT), which provides a theoretical framework to ensure when supervised learning algorithms generalize input data, this manuscript relies on the Algorithmic Stability framework to prove learning bounds for the unsupervised concept drift detection on data streams. Based on such proof, we also designed the Plover algorithm to detect drifts using different measure functions, such as Statistical Moments and the Power Spectrum. In this way, the criterion for issuing data changes can also be adapted to better address the target task. From synthetic and real-world scenarios, we observed that each data stream may require a different measure function to identify concept drifts, according to the underlying characteristics of the corresponding application domain. In addition, we discussed about the differences of our approach against others from literature, and showed illustrative results confirming the usefulness of our proposal. (C) 2018 Elsevier Ltd. All rights reserved.
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