research-article

An adaptive regression tree for non-stationary data streams

Authors:

Ameneh Gholipour,

Mohammad Javad Hosseini,

Hamid BeigyAuthors Info & Claims

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

Pages 815 - 817

https://doi.org/10.1145/2480362.2480519

Published: 18 March 2013 Publication History

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Abstract

Data streams are endless flow of data produced in high speed, large size and usually non-stationary environments. The main property of these streams is the occurrence of concept drifts. Using decision trees is shown to be a powerful approach for accurate and fast learning of data streams. In this paper, we present an incremental regression tree that can predict the target variable of newly incoming instances. The tree is updated in the case of occurring concept drifts either by altering its structure or updating its embedded models. Experimental results show the effectiveness of our algorithm in speed and accuracy aspects in comparison to the best state-of-the-art methods.

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Khannoussi AOlteanu ALabreuche CMeyer P(2021)Simple ranking method using reference profiles: incremental elicitation of the preference parameters4OR10.1007/s10288-021-00487-w20:3(499-530)Online publication date: 20-Jul-2021
https://doi.org/10.1007/s10288-021-00487-w
Zhao XBarber STaylor CMilan Z(2019)Interval forecasts based on regression trees for streaming dataAdvances in Data Analysis and Classification10.1007/s11634-019-00382-7Online publication date: 18-Dec-2019
https://doi.org/10.1007/s11634-019-00382-7
Zhou XLo Faro WZhang XArvapally R(2019)A Framework to Monitor Machine Learning Systems Using Concept Drift DetectionBusiness Information Systems10.1007/978-3-030-20485-3_17(218-231)Online publication date: 18-May-2019
https://doi.org/10.1007/978-3-030-20485-3_17
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Index Terms

An adaptive regression tree for non-stationary data streams
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees

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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]

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Association for Computing Machinery

New York, NY, United States

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Published: 18 March 2013

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SAC '13: SAC '13

March 18 - 22, 2013

Coimbra, Portugal

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

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

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Khannoussi AOlteanu ALabreuche CMeyer P(2021)Simple ranking method using reference profiles: incremental elicitation of the preference parameters4OR10.1007/s10288-021-00487-w20:3(499-530)Online publication date: 20-Jul-2021
https://doi.org/10.1007/s10288-021-00487-w
Zhao XBarber STaylor CMilan Z(2019)Interval forecasts based on regression trees for streaming dataAdvances in Data Analysis and Classification10.1007/s11634-019-00382-7Online publication date: 18-Dec-2019
https://doi.org/10.1007/s11634-019-00382-7
Zhou XLo Faro WZhang XArvapally R(2019)A Framework to Monitor Machine Learning Systems Using Concept Drift DetectionBusiness Information Systems10.1007/978-3-030-20485-3_17(218-231)Online publication date: 18-May-2019
https://doi.org/10.1007/978-3-030-20485-3_17
ZareMoodi PBeigy HKamali Siahroudi S(2018)Novel class detection in data streams using local patterns and neighborhood graphNeurocomputing10.1016/j.neucom.2015.01.037158:C(234-245)Online publication date: 31-Dec-2018
https://dl.acm.org/doi/10.1016/j.neucom.2015.01.037
Hosseini MGholipour ABeigy H(2018)An ensemble of cluster-based classifiers for semi-supervised classification of non-stationary data streamsKnowledge and Information Systems10.1007/s10115-015-0837-446:3(567-597)Online publication date: 30-Dec-2018
https://dl.acm.org/doi/10.1007/s10115-015-0837-4
Duan FDai L(2017)Recognizing the Gradual Changes in sEMG Characteristics Based on Incremental Learning of Wavelet Neural Network EnsembleIEEE Transactions on Industrial Electronics10.1109/TIE.2016.259369364:5(4276-4286)Online publication date: May-2017
https://doi.org/10.1109/TIE.2016.2593693
ZareMoodi PSiahroudi SBeigy HOssowski S(2016)A support vector based approach for classification beyond the learned label space in data streamsProceedings of the 31st Annual ACM Symposium on Applied Computing10.1145/2851613.2851652(910-915)Online publication date: 4-Apr-2016
https://dl.acm.org/doi/10.1145/2851613.2851652

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Decision trees for mining data streams

An Efficient and Sensitive Decision Tree Approach to Mining Concept-Drifting Data Streams

Learning concept-drifting data streams with random ensemble decision trees

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Decision trees for mining data streams

An Efficient and Sensitive Decision Tree Approach to Mining Concept-Drifting Data Streams

Learning concept-drifting data streams with random ensemble decision trees

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