poster

Random rules from data streams

Authors:

Ezilda Almeida,

Petr Kosina,

João GamaAuthors Info & Claims

SAC '13: Proceedings of the 28th Annual ACM Symposium on Applied Computing

Pages 813 - 814

https://doi.org/10.1145/2480362.2480518

Published: 18 March 2013 Publication History

Get Access

Abstract

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 N_att attributes. The proposed algorithm maintains all properties required when learning from stationary data streams: online and any-time classification, processing each example once.

References

[1]

L. Breiman. Random forests. Machine Learnin, 45(1):5--32, 2001.

Digital Library

Google Scholar

[2]

J. Cendrowska. Prism: an algorithm for inducing modular rules. International Journal of Man-Machine Studies, pages 27(4): pp. 349--370, 1987.

Crossref

Google Scholar

[3]

J. Gama and P. Kosina. Learning decision rules from data streams. In IJCAI, pages 1255--1260. AAAI, Menlo Park, USA, 2011.

Digital Library

Google Scholar

[4]

M. A. Bramer. An information-theoretic approach to the pre-pruning of classification rules. In Intelligent Information Processing, pages pp. 201--212, Kluwer, 2002.

Digital Library

Google Scholar

[5]

F. Stahl and M. Bramer. Random prism: An alternative to random forests. In ICITAAI, pages pp. 5--18. Cambridge, UK, 2011.

Crossref

Google Scholar

Cited By

View all

T JS SA S(2023)Feature Drift Detection using Overlapping Window and Mann-Whitney U Test2023 4th International Conference on Innovative Trends in Information Technology (ICITIIT)10.1109/ICITIIT57246.2023.10068710(1-5)Online publication date: 11-Feb-2023
https://doi.org/10.1109/ICITIIT57246.2023.10068710
Helal AOtero F(2022)Data stream classification with ant colony optimisationInternational Journal of Intelligent Systems10.1002/int.2280937:9(5725-5751)Online publication date: 30-Jul-2022
https://dl.acm.org/doi/10.1002/int.22809
Ghafurian MReitter DRitter F(2020)Countdown Timer SpeedACM Transactions on Computer-Human Interaction10.1145/338096127:2(1-25)Online publication date: 11-Mar-2020
https://dl.acm.org/doi/10.1145/3380961
Show More Cited By

Index Terms

Random rules from data streams
1. Computing methodologies
  1. Machine learning

Recommendations

Adaptive Model Rules From High-Speed Data Streams

Decision rules are one of the most expressive and interpretable models for machine learning. In this article, we present Adaptive Model Rules (AMRules), the first stream rule learning algorithm for regression problems. In AMRules, the antecedent of a ...
Very fast decision rules for classification in data streams

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 ...
Adaptive model rules from data streams
ECMLPKDD'13: Proceedings of the 2013th European Conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I

Decision rules are one of the most expressive languages for machine learning. In this paper we present Adaptive Model Rules (AMRules), the first streaming rule learning algorithm for regression problems. In AMRules the antecedent of a rule is a ...

Comments

Information & Contributors

Information

Published In

SAC '13: Proceedings of the 28th Annual ACM Symposium on Applied Computing

March 2013

2124 pages

ISBN:9781450316569

DOI:10.1145/2480362

Conference Chairs:
Sung Y. Shin
South Dakota State University, United States
,
José Carlos Maldonado
ICMC - University of São Paulo, Brazil

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 March 2013

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Poster

Funding Sources

Fundação para a Ciência e a Tecnologia
ERDF through the COMPETE Programme

Conference

SAC '13

Sponsor:

SIGAPP

SAC '13: SAC '13

March 18 - 22, 2013

Coimbra, Portugal

Acceptance Rates

SAC '13 Paper Acceptance Rate 255 of 1,063 submissions, 24%;

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

24
Total Citations
View Citations
163
Total Downloads

Downloads (Last 12 months)8
Downloads (Last 6 weeks)1

Reflects downloads up to 17 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

T JS SA S(2023)Feature Drift Detection using Overlapping Window and Mann-Whitney U Test2023 4th International Conference on Innovative Trends in Information Technology (ICITIIT)10.1109/ICITIIT57246.2023.10068710(1-5)Online publication date: 11-Feb-2023
https://doi.org/10.1109/ICITIIT57246.2023.10068710
Helal AOtero F(2022)Data stream classification with ant colony optimisationInternational Journal of Intelligent Systems10.1002/int.2280937:9(5725-5751)Online publication date: 30-Jul-2022
https://dl.acm.org/doi/10.1002/int.22809
Ghafurian MReitter DRitter F(2020)Countdown Timer SpeedACM Transactions on Computer-Human Interaction10.1145/338096127:2(1-25)Online publication date: 11-Mar-2020
https://dl.acm.org/doi/10.1145/3380961
Baldwin MMankoff JNardi BHayes G(2020)An Activity Centered Approach to Nonvisual Computer InteractionACM Transactions on Computer-Human Interaction10.1145/337421127:2(1-27)Online publication date: 20-Mar-2020
https://dl.acm.org/doi/10.1145/3374211
Alashwal HEl Halaby MCrouse JAbdalla AMoustafa A(2019)The Application of Unsupervised Clustering Methods to Alzheimer’s DiseaseFrontiers in Computational Neuroscience10.3389/fncom.2019.0003113Online publication date: 24-May-2019
https://doi.org/10.3389/fncom.2019.00031
Chamby-Diaz JRecamonde-Mendoza MBazzan A(2019)Dynamic Correlation-Based Feature Selection for Feature Drifts in Data Streams2019 8th Brazilian Conference on Intelligent Systems (BRACIS)10.1109/BRACIS.2019.00043(198-203)Online publication date: Oct-2019
https://doi.org/10.1109/BRACIS.2019.00043
Cain CTrauth E(2017)Black Men in ITACM SIGMIS Database: the DATABASE for Advances in Information Systems10.1145/3084179.308418448:2(35-51)Online publication date: 24-Apr-2017
https://dl.acm.org/doi/10.1145/3084179.3084184
Barddal JGomes HEnembreck FPfahringer B(2017)A survey on feature drift adaptationJournal of Systems and Software10.1016/j.jss.2016.07.005127:C(278-294)Online publication date: 1-May-2017
https://dl.acm.org/doi/10.1016/j.jss.2016.07.005
Kozielski MMatyszok PSikora MWróbel Ł(2017)Decision Rule Learning from Stream of Measurements—A Case Study in Methane Hazard Forecasting in Coal MinesMan-Machine Interactions 510.1007/978-3-319-67792-7_30(301-310)Online publication date: 20-Sep-2017
https://doi.org/10.1007/978-3-319-67792-7_30
Duarte JGama JBifet A(2016)Adaptive Model Rules From High-Speed Data StreamsACM Transactions on Knowledge Discovery from Data10.1145/282995510:3(1-22)Online publication date: 29-Jan-2016
https://dl.acm.org/doi/10.1145/2829955
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Adaptive Model Rules From High-Speed Data Streams

Very fast decision rules for classification in data streams

Adaptive model rules from data streams

Comments

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Funding Sources

Conference

Acceptance Rates

Other Metrics

Article Metrics

Other Metrics

Cited By

Login options

Full Access

PDF

eReader

Abstract

References

Cited By

Index Terms

Recommendations

Adaptive Model Rules From High-Speed Data Streams

Very fast decision rules for classification in data streams

Adaptive model rules from data streams

Comments

Information

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Funding Sources

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Get Access

Login options

Full Access

View options

PDF

eReader

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations