research-article

The Application of SMOTE Algorithm for Unbalanced Data

Authors:

Fan JiangAuthors Info & Claims

AIVR 2018: Proceedings of the 2018 International Conference on Artificial Intelligence and Virtual Reality

Pages 10 - 13

https://doi.org/10.1145/3293663.3293686

Published: 23 November 2018 Publication History

Abstract

The current power user data is unbalanced when it is used to analyze the behavior of the leakage user. In other words, the normal user data and the leakage user data have an inconsistent scale. When the automatic identification model of the leakage user is established, the analysis of the information of the leakage user's behavior feature is not clear, which leads to the reduction of the model's efficiency of classification. In this paper, we use Python and deal with the leakage user data based on SMOTE algorithm to increase the basic information of the users and extract more accurate leakage user behavior characteristics.

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Cited By

Sujatha EUmarani VRekha KGopirajan PManickavasagan V(2024)Handling Imbalanced Data for Credit Card Fraudulent Detection: A Machine Learning ApproachAdvances in Artificial Intelligence and Machine Learning in Big Data Processing10.1007/978-3-031-73068-9_18(220-233)Online publication date: 1-Oct-2024
https://doi.org/10.1007/978-3-031-73068-9_18
A'la FFirdaus NHartatik Safi'Ie M(2023)SMOTE on Numeric Breast Cancer Dataset to Overcome Imbalance Class2023 6th International Conference of Computer and Informatics Engineering (IC2IE)10.1109/IC2IE60547.2023.10331221(335-339)Online publication date: 14-Sep-2023
https://doi.org/10.1109/IC2IE60547.2023.10331221
Wang GWang JHe K(2022)Majority-to-minority resampling for boosting-based classification under imbalanced dataApplied Intelligence10.1007/s10489-022-03585-253:4(4541-4562)Online publication date: 11-Jun-2022
https://doi.org/10.1007/s10489-022-03585-2
Show More Cited By

Index Terms

The Application of SMOTE Algorithm for Unbalanced Data
1. Information systems
  1. Data management systems
    1. Information integration
      1. Data cleaning

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AIVR 2018: Proceedings of the 2018 International Conference on Artificial Intelligence and Virtual Reality

November 2018

144 pages

ISBN:9781450366410

DOI:10.1145/3293663

Copyright © 2018 ACM.

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University of Tsukuba: University of Tsukuba

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Association for Computing Machinery

New York, NY, United States

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Published: 23 November 2018

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AIVR 2018

AIVR 2018: 2018 International Conference on Artificial Intelligence and Virtual Reality

November 23 - 25, 2018

Nagoya, Japan

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Cited By

Sujatha EUmarani VRekha KGopirajan PManickavasagan V(2024)Handling Imbalanced Data for Credit Card Fraudulent Detection: A Machine Learning ApproachAdvances in Artificial Intelligence and Machine Learning in Big Data Processing10.1007/978-3-031-73068-9_18(220-233)Online publication date: 1-Oct-2024
https://doi.org/10.1007/978-3-031-73068-9_18
A'la FFirdaus NHartatik Safi'Ie M(2023)SMOTE on Numeric Breast Cancer Dataset to Overcome Imbalance Class2023 6th International Conference of Computer and Informatics Engineering (IC2IE)10.1109/IC2IE60547.2023.10331221(335-339)Online publication date: 14-Sep-2023
https://doi.org/10.1109/IC2IE60547.2023.10331221
Wang GWang JHe K(2022)Majority-to-minority resampling for boosting-based classification under imbalanced dataApplied Intelligence10.1007/s10489-022-03585-253:4(4541-4562)Online publication date: 11-Jun-2022
https://doi.org/10.1007/s10489-022-03585-2
Stanzione ARicciardi CCuocolo RRomeo VPetrone JSarnataro MMainenti PImprota GDe Rosa FInsabato LBrunetti AMaurea S(2020)MRI Radiomics for the Prediction of Fuhrman Grade in Clear Cell Renal Cell Carcinoma: a Machine Learning Exploratory StudyJournal of Digital Imaging10.1007/s10278-020-00336-y33:4(879-887)Online publication date: 20-Apr-2020
https://doi.org/10.1007/s10278-020-00336-y
Wang HGuan WZhang K(2020)Over-Sampling Multi-classification Method Based on Centroid SpaceBig Data and Security10.1007/978-981-15-7530-3_47(616-632)Online publication date: 14-Aug-2020
https://doi.org/10.1007/978-981-15-7530-3_47
Ibrahim ZBader-El-Den MCocea M(2019)Improving Imbalanced Students’ Text Feedback Classification Using Re-sampling Based ApproachAdvances in Computational Intelligence Systems10.1007/978-3-030-29933-0_22(262-267)Online publication date: 30-Aug-2019
https://doi.org/10.1007/978-3-030-29933-0_22

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