Very Fast Decision Rules for Multi-class Problems

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F12%3A00059330" target="_blank" >RIV/00216224:14330/12:00059330 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Very Fast Decision Rules for Multi-class Problems

Popis výsledku v původním jazyce

Decision rules are one of the most interpretable and flexible models for data mining prediction tasks. Till now, few works presented on-line, any-time and one-pass algorithms for learning decision rules in the stream mining scenario. A quite recent algorithm, the Very Fast Decision Rules (VFDR), learns set of rules, where each rule discriminates one class from all the other. In this work we extend the VFDR algorithm by decomposing a multi-class problem into a set of two-class problems and inducing a setof discriminative rules for each binary problem. The proposed algorithm maintains all properties required when learning from stationary data streams: on-line and any-time classifiers, processing each example once. Moreover, it is able to learn ordered and unordered rule sets. The new approach is evaluated on various real and artificial datasets. The new algorithm improves the performance of the previous version and is competitive with the state-of-the-art decision tree learning method f

Název v anglickém jazyce

Very Fast Decision Rules for Multi-class Problems

Popis výsledku anglicky

Decision rules are one of the most interpretable and flexible models for data mining prediction tasks. Till now, few works presented on-line, any-time and one-pass algorithms for learning decision rules in the stream mining scenario. A quite recent algorithm, the Very Fast Decision Rules (VFDR), learns set of rules, where each rule discriminates one class from all the other. In this work we extend the VFDR algorithm by decomposing a multi-class problem into a set of two-class problems and inducing a setof discriminative rules for each binary problem. The proposed algorithm maintains all properties required when learning from stationary data streams: on-line and any-time classifiers, processing each example once. Moreover, it is able to learn ordered and unordered rule sets. The new approach is evaluated on various real and artificial datasets. The new algorithm improves the performance of the previous version and is competitive with the state-of-the-art decision tree learning method f

Druh

D - Stať ve sborníku

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

2012

Kód důvěrnosti údajů

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

Název statě ve sborníku

ACM 27th Symposium On Applied Computing

ISBN

9781450308571

ISSN

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e-ISSN

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Počet stran výsledku

6

Strana od-do

795-800

Název nakladatele

ACM

Místo vydání

New York, NY, USA

Místo konání akce

Riva del Garda, Italy

Datum konání akce

26. 3. 2012

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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