Random rules from data streams

D 2013

Random rules from data streams

EZILDA, Almeida, Petr KOSINA and Joao GAMA

Basic information

Original name

Random rules from data streams

Authors

EZILDA, Almeida (620 Portugal), Petr KOSINA (203 Czech Republic, guarantor, belonging to the institution) and Joao GAMA (620 Portugal)

Edition

New York, NY, USA, Proceedings of the 28th Annual ACM Symposium on Applied Computing, SAC '13, p. 813-814, 2 pp. 2013

Publisher

ACM

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

electronic version available online

References:

URL

RIV identification code

RIV/00216224:14330/13:00068420

Organization unit

Faculty of Informatics

ISBN

978-1-4503-1656-9

DOI

http://dx.doi.org/10.1145/2480362.2480518

Keywords in English

Data Streams; Classification; Rule Learning; Random Rules

Abstract

V originále

Existing works suggest that random inputs and random features produce good results in classification. In this paper we study the problem of generating random rule sets from data streams. One of the most interpretable and flexible models for data stream mining prediction tasks is the Very Fast Decision Rules learner (VFDR). In this work we extend the VFDR algorithm using random rules from data streams. The proposed algorithm generates several sets of rules. Each rule set is associated with a set of Natt attributes. The proposed algorithm maintains all properties required when learning from stationary data streams: online and any-time classification, processing each example once.

Links

LG13010, research and development project

Name: Zastoupení ČR v European Research Consortium for Informatics and Mathematics (Acronym: ERCIM-CZ)

Investor: Ministry of Education, Youth and Sports of the CR

MUNI/A/0758/2011, interní kód MU

Name: Zapojení studentů Fakulty informatiky do mezinárodní vědecké komunity (Acronym: SKOMU)

Investor: Masaryk University, Category A

Citovat

EZILDA, Almeida, Petr KOSINA and Joao GAMA. Random rules from data streams. Online. In Sung Y. Shin, Jose Carlos Maldonado. Proceedings of the 28th Annual ACM Symposium on Applied Computing, SAC '13. New York, NY, USA: ACM, 2013, p. 813-814. ISBN 978-1-4503-1656-9. Available from: https://dx.doi.org/10.1145/2480362.2480518.

@inproceedings{1108864,
   author = {Ezilda, Almeida and Kosina, Petr and Gama, Joao},
   address = {New York, NY, USA},
   booktitle = {Proceedings of the 28th Annual ACM Symposium on Applied Computing, SAC '13},
   doi = {http://dx.doi.org/10.1145/2480362.2480518},
   editor = {Sung Y. Shin, Jose Carlos Maldonado},
   keywords = {Data Streams; Classification; Rule Learning; Random Rules},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {New York, NY, USA},
   isbn = {978-1-4503-1656-9},
   pages = {813-814},
   publisher = {ACM},
   title = {Random rules from data streams},
   url = {http://doi.acm.org/10.1145/2480362.2480518},
   year = {2013}
}

TY  - JOUR
ID  - 1108864
AU  - Ezilda, Almeida - Kosina, Petr - Gama, Joao
PY  - 2013
TI  - Random rules from data streams
PB  - ACM
CY  - New York, NY, USA
SN  - 9781450316569
KW  - Data Streams
KW  - Classification
KW  - Rule Learning
KW  - Random Rules
UR  - http://doi.acm.org/10.1145/2480362.2480518
N2  - Existing works suggest that random inputs and random features produce good results in classification. In this paper we study the problem of generating random rule sets from data streams. One of the most interpretable and flexible models for data stream mining prediction tasks is the Very Fast Decision Rules learner (VFDR). In this work we extend the VFDR algorithm using random rules from data streams. The proposed algorithm generates several sets of rules. Each rule set is associated with a set of Natt attributes. The proposed algorithm maintains all properties required when learning from stationary data streams: online and any-time classification, processing each example once.
ER  -

EZILDA, Almeida, Petr KOSINA and Joao GAMA. Random rules from data streams. Online. In Sung Y. Shin, Jose Carlos Maldonado. \textit{Proceedings of the 28th Annual ACM Symposium on Applied Computing, SAC '13}. New York, NY, USA: ACM, 2013, p.~813-814. ISBN~978-1-4503-1656-9. Available from: https://dx.doi.org/10.1145/2480362.2480518.

Detailed Information on Publication Record