Very Fast Decision Rules for Classification in Data Streams

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F15%3A00081934" target="_blank" >RIV/00216224:14330/15:00081934 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/s10618-013-0340-z" target="_blank" >http://dx.doi.org/10.1007/s10618-013-0340-z</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10618-013-0340-z" target="_blank" >10.1007/s10618-013-0340-z</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Very Fast Decision Rules for Classification in Data Streams

Popis výsledku v původním jazyce

Data stream mining is the process of extracting knowledge structures from continuous, rapid data records. Many decision tasks can be formulated as stream mining problems and therefore many new algorithms for data streams are being proposed. Decision rules are one of the most interpretable and flexible models for predictive data mining. Nevertheless, few algorithms have been proposed in the literature to learn rule models for time-changing and high-speed flows of data. In this paper we present the very fast decision rules (VFDR) algorithm and discuss interesting extensions to the base version. All the proposed versions are one-pass and any-time algorithms. They work on-line and learn ordered or unordered rule sets. Algorithms designed to work with datastreams should be able to detect changes and quickly adapt the decision model. In order to manage these situations we also present the adaptive extension (AVFDR) to detect changes in the process generating data and adapt the decision mode

Název v anglickém jazyce

Very Fast Decision Rules for Classification in Data Streams

Popis výsledku anglicky

Data stream mining is the process of extracting knowledge structures from continuous, rapid data records. Many decision tasks can be formulated as stream mining problems and therefore many new algorithms for data streams are being proposed. Decision rules are one of the most interpretable and flexible models for predictive data mining. Nevertheless, few algorithms have been proposed in the literature to learn rule models for time-changing and high-speed flows of data. In this paper we present the very fast decision rules (VFDR) algorithm and discuss interesting extensions to the base version. All the proposed versions are one-pass and any-time algorithms. They work on-line and learn ordered or unordered rule sets. Algorithms designed to work with datastreams should be able to detect changes and quickly adapt the decision model. In order to manage these situations we also present the adaptive extension (AVFDR) to detect changes in the process generating data and adapt the decision mode

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/LA09016" target="_blank" >LA09016: Účast ČR v European Research Consortium for Informatics and Mathematics (ERCIM)</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název periodika

Data Mining and Knowledge Discovery

ISSN

1384-5810

e-ISSN

—

Svazek periodika

29

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

35

Strana od-do

168-202

Kód UT WoS článku

000347948900006

EID výsledku v databázi Scopus

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