Recurrent concepts in data streams classification

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F13%3A00068485" target="_blank" >RIV/00216224:14330/13:00068485 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/s10115-013-0654-6" target="_blank" >http://dx.doi.org/10.1007/s10115-013-0654-6</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10115-013-0654-6" target="_blank" >10.1007/s10115-013-0654-6</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Recurrent concepts in data streams classification

Popis výsledku v původním jazyce

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 ableto 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.

Název v anglickém jazyce

Recurrent concepts in data streams classification

Popis výsledku anglicky

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 ableto 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.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/LG13010" target="_blank" >LG13010: Zastoupení ČR v European Research Consortium for Informatics and Mathematics</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2013

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Knowledge and Information Systems

ISSN

0219-1377

e-ISSN

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Svazek periodika

roč. 2013

Číslo periodika v rámci svazku

May

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

19

Strana od-do

1-19

Kód UT WoS článku

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EID výsledku v databázi Scopus

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