IDEAS home Printed from https://ideas.repec.org/a/spr/aodasc/v10y2023i2d10.1007_s40745-022-00387-8.html
   My bibliography  Save this article

A Determining Degree-Based Method for Classification Problems with Interval-Valued Attributes

Author

Listed:
  • Xueyan Xu

    (Beijing Normal University)

  • Fusheng Yu

    (Beijing Normal University)

  • Runjun Wan

    (Liaoning Technical University)

Abstract

The determining degree-based classification methods, new types of classification methods built in the frame of factor space theory, mainly include factorial analysis, improved factorial analysis and set subtraction and rotation calculation (S&R). This paper first compares the three methods to present a comprehensive understanding of them and claims that whether to reuse dominant factors and to use synthetic partitioning are the main differences between factorial analysis and S&R. Furthermore, this paper introduces S&R definitively and concisely through an example. Based on the investigation, we propose a novel method for classification problems with interval-valued attributes that uses a determining degree to discretize interval values, and takes S&R as one of its steps. Experimental results show that this method is effective and reasonable.

Suggested Citation

  • Xueyan Xu & Fusheng Yu & Runjun Wan, 2023. "A Determining Degree-Based Method for Classification Problems with Interval-Valued Attributes," Annals of Data Science, Springer, vol. 10(2), pages 393-413, April.
  • Handle: RePEc:spr:aodasc:v:10:y:2023:i:2:d:10.1007_s40745-022-00387-8
    DOI: 10.1007/s40745-022-00387-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40745-022-00387-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s40745-022-00387-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Feng Liu & Yong Shi, 2020. "Investigating Laws of Intelligence Based on AI IQ Research," Annals of Data Science, Springer, vol. 7(3), pages 399-416, September.
    2. James M. Tien, 2017. "Internet of Things, Real-Time Decision Making, and Artificial Intelligence," Annals of Data Science, Springer, vol. 4(2), pages 149-178, June.
    3. Peizhuang Wang & He Ouyang & Yixin Zhong & Huacan He, 2016. "Cognition Math Based on Factor Space," Annals of Data Science, Springer, vol. 3(3), pages 281-303, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Anda Tang & Pei Quan & Lingfeng Niu & Yong Shi, 2022. "A Survey for Sparse Regularization Based Compression Methods," Annals of Data Science, Springer, vol. 9(4), pages 695-722, August.
    2. Xingsen Li & Junlin Zeng & Haitao Liu & Peizhuang Wang, 2022. "Intelligent Problem Solving Model and its Cross Research Directions Based on Factor Space and Extenics," Annals of Data Science, Springer, vol. 9(3), pages 469-484, June.
    3. Hui Sun & Fanhui Zeng & Yang Yang, 2022. "Covert Factor’s Exploiting and Factor Planning," Annals of Data Science, Springer, vol. 9(3), pages 449-467, June.
    4. Binxiang Jiang, 2022. "Research on Factor Space Engineering and Application of Evidence Factor Mining in Evidence-based Reconstruction," Annals of Data Science, Springer, vol. 9(3), pages 503-537, June.
    5. Yundong Gu & Dongfen Ma & Jiawei Cui & Zhenhua Li & Yaqi Chen, 2022. "Variable-Weighted Ensemble Forecasting of Short-Term Power Load Based on Factor Space Theory," Annals of Data Science, Springer, vol. 9(3), pages 485-501, June.
    6. Pejman Gholami-Dastgerdi & Mohammad-Reza Feizi-Derakhshi, 2023. "Part of Speech Tagging Using Part of Speech Sequence Graph," Annals of Data Science, Springer, vol. 10(5), pages 1301-1328, October.
    7. Tiejun Cui & Peizhuang Wang & Shasha Li, 2022. "Research on Uncertainty of System Function State from Factors-Data-Cognition," Annals of Data Science, Springer, vol. 9(3), pages 593-609, June.
    8. Xiangfu Meng & Jing Wen & Jiasheng Shi & Zihan Li & Jinxia Zhu & Peizhuang Wang, 2022. "Factor Query Language (FQL): A Fundamental Language for the Next Generation of Intelligent Database," Annals of Data Science, Springer, vol. 9(3), pages 539-554, June.
    9. Elton G. Aráujo & Julio C. S. Vasconcelos & Denize P. Santos & Edwin M. M. Ortega & Dalton Souza & João P. F. Zanetoni, 2023. "The Zero-Inflated Negative Binomial Semiparametric Regression Model: Application to Number of Failing Grades Data," Annals of Data Science, Springer, vol. 10(4), pages 991-1006, August.
    10. Heba Soltan Mohamed & M. Masoom Ali & Haitham M. Yousof, 2023. "The Lindley Gompertz Model for Estimating the Survival Rates: Properties and Applications in Insurance," Annals of Data Science, Springer, vol. 10(5), pages 1199-1216, October.
    11. Roberto Moro-Visconti & Salvador Cruz Rambaud & Joaquín López Pascual, 2023. "Artificial intelligence-driven scalability and its impact on the sustainability and valuation of traditional firms," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-14, December.
    12. M. Sridharan, 2023. "Generalized Regression Neural Network Model Based Estimation of Global Solar Energy Using Meteorological Parameters," Annals of Data Science, Springer, vol. 10(4), pages 1107-1125, August.
    13. Qinghua Zheng & Chutong Yang & Haijun Yang & Jianhe Zhou, 2020. "A Fast Exact Algorithm for Deployment of Sensor Nodes for Internet of Things," Information Systems Frontiers, Springer, vol. 22(4), pages 829-842, August.
    14. Prashant Singh & Prashant Verma & Nikhil Singh, 2022. "Offline Signature Verification: An Application of GLCM Features in Machine Learning," Annals of Data Science, Springer, vol. 9(6), pages 1309-1321, December.
    15. Shah Hussain & Muhammad Qasim Khan, 2023. "Student-Performulator: Predicting Students’ Academic Performance at Secondary and Intermediate Level Using Machine Learning," Annals of Data Science, Springer, vol. 10(3), pages 637-655, June.
    16. A. R. Sherwani & Q. M. Ali, 2023. "Parametric Classification using Fuzzy Approach for Handling the Problem of Mixed Pixels in Ground Truth Data for a Satellite Image," Annals of Data Science, Springer, vol. 10(6), pages 1459-1472, December.
    17. Hui Zheng & Peng LI & Jing HE, 2022. "A Novel Association Rule Mining Method for Streaming Temporal Data," Annals of Data Science, Springer, vol. 9(4), pages 863-883, August.
    18. Rakhal Das & Anjan Mukherjee & Binod Chandra Tripathy, 2022. "Application of Neutrosophic Similarity Measures in Covid-19," Annals of Data Science, Springer, vol. 9(1), pages 55-70, February.
    19. Muhammed Navas Thorakkattle & Shazia Farhin & Athar Ali khan, 2022. "Forecasting the Trends of Covid-19 and Causal Impact of Vaccines Using Bayesian Structural time Series and ARIMA," Annals of Data Science, Springer, vol. 9(5), pages 1025-1047, October.
    20. Siying Guo & Jianxuan Liu & Qiu Wang, 2022. "Effective Learning During COVID-19: Multilevel Covariates Matching and Propensity Score Matching," Annals of Data Science, Springer, vol. 9(5), pages 967-982, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:aodasc:v:10:y:2023:i:2:d:10.1007_s40745-022-00387-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.