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Active learning using adaptive resampling

Authors:

Vijay S. Iyengar,

Chidanand Apte,

Tong ZhangAuthors Info & Claims

KDD '00: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 91 - 98

https://doi.org/10.1145/347090.347110

Published: 01 August 2000 Publication History

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Cited By

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Cheng MCheng XZhou F(2024)Query Learning on the Semisupervised Margin2024 IEEE 9th International Conference on Computational Intelligence and Applications (ICCIA)10.1109/ICCIA62557.2024.10719315(5-10)Online publication date: 9-Aug-2024
https://doi.org/10.1109/ICCIA62557.2024.10719315
Karl Ferdinand Loder A(2023)Predicting the Number of “Active” Students: A Method for Preventive University ManagementJournal of College Student Retention: Research, Theory & Practice10.1177/15210251231201394Online publication date: 14-Sep-2023
https://doi.org/10.1177/15210251231201394
Wu YLi XZhang XKang YSun CLiu XFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Community-Based Hierarchical Positive-Unlabeled (PU) Model Fusion for Chronic Disease PredictionProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614818(2747-2756)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614818
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Index Terms

Active learning using adaptive resampling
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
2. Information systems
  1. Information systems applications
    1. Data mining

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Published In

KDD '00: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining

August 2000

537 pages

ISBN:1581132336

DOI:10.1145/347090

Chairmen:
Raghu Ramakrishnan
Univ. of Wisconsin
,
Sal Stolfo
Columbia Univ., New York, NY
,
Roberto Bayardo
IBM Almaden Research Center, San Jose, CA
,
Ismail Parsa
Epsilon

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

Publication History

Published: 01 August 2000

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KDD00

Sponsor:

KDD00: The Second Annual International Conference on Knowledge Discovery in Data

August 20 - 23, 2000

Massachusetts, Boston, USA

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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KDD '25

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August 3 - 7, 2025

Toronto , ON , Canada

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Cited By

View all

Cheng MCheng XZhou F(2024)Query Learning on the Semisupervised Margin2024 IEEE 9th International Conference on Computational Intelligence and Applications (ICCIA)10.1109/ICCIA62557.2024.10719315(5-10)Online publication date: 9-Aug-2024
https://doi.org/10.1109/ICCIA62557.2024.10719315
Karl Ferdinand Loder A(2023)Predicting the Number of “Active” Students: A Method for Preventive University ManagementJournal of College Student Retention: Research, Theory & Practice10.1177/15210251231201394Online publication date: 14-Sep-2023
https://doi.org/10.1177/15210251231201394
Wu YLi XZhang XKang YSun CLiu XFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Community-Based Hierarchical Positive-Unlabeled (PU) Model Fusion for Chronic Disease PredictionProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614818(2747-2756)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614818
Tharwat ASchenck W(2022)A Novel Low-Query-Budget Active Learner with Pseudo-Labels for Imbalanced DataMathematics10.3390/math1007106810:7(1068)Online publication date: 26-Mar-2022
https://doi.org/10.3390/math10071068
Kyeremeh FZhi FYi YGyamfi ENti I(2022)Solar PV Power Forecasting with a Hybrid LSTM-AdaBoost Ensemble2022 IEEE/IET International Utility Conference and Exposition (IUCE)10.1109/IUCE55902.2022.10079424(1-7)Online publication date: 3-Nov-2022
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Alizadeh ASinghal MBehzadan VTavallali PRanganath A(2022)Stochastic Induction of Decision Trees with Application to Learning Haar Trees2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)10.1109/ICMLA55696.2022.00137(825-830)Online publication date: Dec-2022
https://doi.org/10.1109/ICMLA55696.2022.00137
Ben Hamida SHmida HBorgi ARukoz M(2021)Adaptive sampling for active learning with genetic programmingCognitive Systems Research10.1016/j.cogsys.2020.08.00865(23-39)Online publication date: Jan-2021
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Lu WMa CLi P(2020)Research on Sample Selection of Urban Rail Transit Passenger Flow Forecasting Based on SCBP AlgorithmIEEE Access10.1109/ACCESS.2020.29935958(89425-89438)Online publication date: 2020
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Tharwat ASchenck W(2020)Balancing Exploration and Exploitation: A novel active learner for imbalanced dataKnowledge-Based Systems10.1016/j.knosys.2020.106500210(106500)Online publication date: Dec-2020
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Kalaivani KMaheswari N(2017)Predicting disease using information integration platform for large data2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS)10.1109/ICECDS.2017.8389581(971-975)Online publication date: Aug-2017
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