Detailed Information on Publication Record
2013
Recurrent concepts in data streams classification
GAMA, João and Petr KOSINABasic information
Original name
Recurrent concepts in data streams classification
Authors
GAMA, João (620 Portugal) and Petr KOSINA (203 Czech Republic, guarantor, belonging to the institution)
Edition
Knowledge and Information Systems, London, Springer-Verlag, 2013, 0219-1377
Other information
Language
English
Type of outcome
Článek v odborném periodiku
Field of Study
10201 Computer sciences, information science, bioinformatics
Country of publisher
United Kingdom of Great Britain and Northern Ireland
Confidentiality degree
není předmětem státního či obchodního tajemství
References:
Impact factor
Impact factor: 2.639
RIV identification code
RIV/00216224:14330/13:00068485
Organization unit
Faculty of Informatics
UT WoS
000340616300001
Keywords in English
Data streams; Concept drift; Meta-learning; Recurrent concepts
Tags
International impact, Reviewed
Změněno: 23/10/2017 10:24, RNDr. Pavel Šmerk, Ph.D.
Abstract
V originále
This work addresses the problem of mining data streams generated in dynamic environments where the distribution underlying the observations may change over time. We present a system that monitors the evolution of the learning process. The system is able to self-diagnose degradations of this process, using change detection mechanisms, and self-repair the decision models. The system uses meta-learning techniques that characterize the domain of applicability of previously learned models. The meta-learner can detect recurrence of contexts, using unlabeled examples, and take pro-active actions by activating previously learned models. The experimental evaluation on three text mining problems demonstrates the main advantages of the proposed system: it provides information about the recurrence of concepts and rapidly adapts decision models when drift occurs.
Links
| LG13010, research and development project |
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| MUNI/A/0758/2011, interní kód MU |
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