Handling Time Changing Data with Adaptive Very Fast Decision
Rules

D 2012

Handling Time Changing Data with Adaptive Very Fast Decision Rules

KOSINA, Petr a Joao GAMA

Základní údaje

Originální název

Handling Time Changing Data with Adaptive Very Fast Decision Rules

Autoři

KOSINA, Petr (203 Česká republika, garant, domácí) a Joao GAMA (620 Portugalsko)

Vydání

Berlin / Heidelberg, Machine Learning and Knowledge Discovery in Databases ECML/PKDD, od s. 827-842, 16 s. 2012

Nakladatel

Springer Berlin / Heidelberg

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Česká republika

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

tištěná verze "print"

Odkazy

URL

Impakt faktor

Impact factor: 0.402 v roce 2005

Kód RIV

RIV/00216224:14330/12:00061019

Organizační jednotka

Fakulta informatiky

ISBN

978-3-642-33459-7

ISSN

DOI

http://dx.doi.org/10.1007/978-3-642-33460-3_58

Klíčová slova anglicky

Data Streams; Decision Rules; Concept Drift

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 23. 4. 2013 13:24, RNDr. Pavel Šmerk, Ph.D.

Anotace

V originále

Data streams are usually characterized by changes in the underlying distribution generating data. Therefore algorithms designed to work with data streams should be able to detect changes and quickly adapt the decision model. Rules are one of the most interpretable and flexible models for data mining prediction tasks. In this paper we present the Adaptive Very Fast Decision Rules (AVFDR), an on-line, any-time and one-pass algorithm for learning decision rules in the context of time changing data. AVFDR can learn ordered and unordered rule sets. It is able to adapt the decision model via incremental induction and specialization of rules. Detecting local drifts takes advantage of the modularity of rule sets. In AVFDR, each individual rule monitors the evolution of performance metrics to detect concept drift. AVFDR prunes rules that detect drift. This explicit change detection mechanism provides useful information about the dynamics of the process generating data, faster adaption to changes and generates compact rule sets. The experimental evaluation shows this method is able to learn fast and compact rule sets from evolving streams in comparison to alternative methods.

Návaznosti

LA09016, projekt VaV

Název: Účast ČR v European Research Consortium for Informatics and Mathematics (ERCIM) (Akronym: ERCIM)

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, Účast ČR v European Research Consortium for Informatics and Mathematics

MUNI/A/0758/2011, interní kód MU

Název: Zapojení studentů Fakulty informatiky do mezinárodní vědecké komunity (Akronym: SKOMU)

Investor: Masarykova univerzita, Zapojení studentů Fakulty informatiky do mezinárodní vědecké komunity, DO R. 2020_Kategorie A - Specifický výzkum - Studentské výzkumné projekty

Citovat

KOSINA, Petr a Joao GAMA. Handling Time Changing Data with Adaptive Very Fast Decision Rules. In Peter Flach, Tijl De Bie, Nello Cristianini. Machine Learning and Knowledge Discovery in Databases ECML/PKDD. Berlin / Heidelberg: Springer Berlin / Heidelberg, 2012, s. 827-842. ISBN 978-3-642-33459-7. Dostupné z: https://dx.doi.org/10.1007/978-3-642-33460-3_58.

@inproceedings{993086,
   author = {Kosina, Petr and Gama, Joao},
   address = {Berlin / Heidelberg},
   booktitle = {Machine Learning and Knowledge Discovery in Databases ECML/PKDD},
   doi = {http://dx.doi.org/10.1007/978-3-642-33460-3_58},
   editor = {Peter Flach, Tijl De Bie, Nello Cristianini},
   keywords = {Data Streams; Decision Rules; Concept Drift},
   howpublished = {tištěná verze "print"},
   language = {eng},
   location = {Berlin / Heidelberg},
   isbn = {978-3-642-33459-7},
   pages = {827-842},
   publisher = {Springer Berlin / Heidelberg},
   title = {Handling Time Changing Data with Adaptive Very Fast Decision Rules},
   url = {http://dx.doi.org/10.1007/978-3-642-33460-3_58},
   year = {2012}
}

TY  - JOUR
ID  - 993086
AU  - Kosina, Petr - Gama, Joao
PY  - 2012
TI  - Handling Time Changing Data with Adaptive Very Fast Decision Rules
PB  - Springer Berlin / Heidelberg
CY  - Berlin / Heidelberg
SN  - 9783642334597
KW  - Data Streams
KW  - Decision Rules
KW  - Concept Drift
UR  - http://dx.doi.org/10.1007/978-3-642-33460-3_58
L2  - http://dx.doi.org/10.1007/978-3-642-33460-3_58
N2  - Data streams are usually characterized by changes in the underlying distribution generating data. Therefore algorithms designed to work with data streams should be able to detect changes and quickly adapt the decision model. Rules are one of the most interpretable and flexible models for data mining prediction tasks. In this paper we present the Adaptive Very Fast Decision Rules (AVFDR), an on-line, any-time and one-pass algorithm for learning decision rules in the context of time changing data. AVFDR can learn ordered and unordered rule sets. It is able to adapt the decision model via incremental induction and specialization of rules. Detecting local drifts takes advantage of the modularity of rule sets. In AVFDR, each individual rule monitors the evolution of performance metrics to detect concept drift. AVFDR prunes rules that detect drift. This explicit change detection mechanism provides useful information about the dynamics of the process generating data, faster adaption to changes and generates compact rule sets. The experimental evaluation shows this method is able to learn fast and compact rule sets from evolving streams in comparison to alternative methods.
ER  -

KOSINA, Petr a Joao GAMA. Handling Time Changing Data with Adaptive Very Fast Decision Rules. In Peter Flach, Tijl De Bie, Nello Cristianini. \textit{Machine Learning and Knowledge Discovery in Databases ECML/PKDD}. Berlin / Heidelberg: Springer Berlin / Heidelberg, 2012, s.~827-842. ISBN~978-3-642-33459-7. Dostupné z: https://dx.doi.org/10.1007/978-3-642-33460-3\_{}58.

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