IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v13y2025i7p1201-d1628661.html
   My bibliography  Save this article

Feedforward Factorial Hidden Markov Model

Author

Listed:
  • Zhongxing Peng

    (School of Computer Information Engineering, Hanshan Normal University, Chaozhou 521041, China)

  • Wei Huang

    (School of Computer Information Engineering, Hanshan Normal University, Chaozhou 521041, China)

  • Yinghui Zhu

    (School of Computer Information Engineering, Hanshan Normal University, Chaozhou 521041, China)

Abstract

This paper introduces a novel kind of factorial hidden Markov model (FHMM), specifically the feedforward FHMM (FFHMM). In contrast to traditional FHMMs, the FFHMM is capable of directly utilizing supplementary information from observations through predefined states, which are derived using an automatic feature filter (AFF). We investigate two variations of FFHMM models that integrate predefined states with the FHMM: the direct FFHMM and the embedded FFHMM. In the direct FFHMM, alterations to one sub-hidden Markov model (HMM) do not affect the others, enabling individual improvements in HMM estimation. On the other hand, the sub-HMM chains within the embedded FFHMM are interconnected, suggesting that adjustments to one HMM chain may enhance the estimations of other HMM chains. Consequently, we propose two algorithms for these FFHMM models to estimate their respective hidden states. Ultimately, experiments conducted on two real-world datasets validate the efficacy of the proposed models and algorithms.

Suggested Citation

  • Zhongxing Peng & Wei Huang & Yinghui Zhu, 2025. "Feedforward Factorial Hidden Markov Model," Mathematics, MDPI, vol. 13(7), pages 1-20, April.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:7:p:1201-:d:1628661
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/13/7/1201/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/13/7/1201/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. David L. Olson & Dursun Delen, 2008. "Advanced Data Mining Techniques," Springer Books, Springer, number 978-3-540-76917-0, January.
    2. Amirali Kani & Wayne S. DeSarbo & Duncan K. H. Fong, 2018. "A Factorial Hidden Markov Model for the Analysis of Temporal Change in Choice Models," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 5(3), pages 162-177, December.
    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. Mark Gilchrist & Deana Lehmann Mooers & Glenn Skrubbeltrang & Francine Vachon, 2012. "Knowledge Discovery in Databases for Competitive Advantage," Journal of Management and Strategy, Journal of Management and Strategy, Sciedu Press, vol. 3(2), pages 2-15, April.
    2. Marina Johnson & Abdullah Albizri & Serhat Simsek, 2022. "Artificial intelligence in healthcare operations to enhance treatment outcomes: a framework to predict lung cancer prognosis," Annals of Operations Research, Springer, vol. 308(1), pages 275-305, January.
    3. Simsek, Serhat & Dag, Ali & Tiahrt, Thomas & Oztekin, Asil, 2021. "A Bayesian Belief Network-based probabilistic mechanism to determine patient no-show risk categories," Omega, Elsevier, vol. 100(C).
    4. Sebastian Büsch & Volker Nissen & Arndt Wünscher, 0. "Automatic classification of data-warehouse-data for information lifecycle management using machine learning techniques," Information Systems Frontiers, Springer, vol. 0, pages 1-15.
    5. Yucel, Ahmet & Dag, Ali & Oztekin, Asil & Carpenter, Mark, 2022. "A novel text analytic methodology for classification of product and service reviews," Journal of Business Research, Elsevier, vol. 151(C), pages 287-297.
    6. Kizilaslan, Recep & Freund, Steven & Iseri, Ali, 2016. "A data analytic approach to forecasting daily stock returns in an emerging marketAuthor-Name: Oztekin, Asil," European Journal of Operational Research, Elsevier, vol. 253(3), pages 697-710.
    7. Marina Johnson & Abdullah Albizri & Antoine Harfouche, 2023. "Responsible Artificial Intelligence in Healthcare: Predicting and Preventing Insurance Claim Denials for Economic and Social Wellbeing," Information Systems Frontiers, Springer, vol. 25(6), pages 2179-2195, December.
    8. Saljooghi, Saeed & Safisamghabadib, Azamdokht, 2016. "Analyzing Semiconductor component's market sales data to create an Expert Fuzzy inference system," MPRA Paper 79846, University Library of Munich, Germany.
    9. Nurettin Oner & Ferhat D. Zengul & Ismail Agirbas, 2024. "Evaluation of the Financial Distress of Hospitals Through Machine Learning: An Application of AI in Healthcare Industry," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 31(4), December.
    10. Ramin Vakili & Mojdeh Khorsand, 2022. "A Machine Learning-Based Method for Identifying Critical Distance Relays for Transient Stability Studies," Energies, MDPI, vol. 15(23), pages 1-28, November.
    11. Delen, Dursun & Cogdell, Douglas & Kasap, Nihat, 2012. "A comparative analysis of data mining methods in predicting NCAA bowl outcomes," International Journal of Forecasting, Elsevier, vol. 28(2), pages 543-552.
    12. Chen, Kunlong & Zheng, Fangdan & Jiang, Jiuchun & Zhang, Weige & Jiang, Yan & Chen, Kunjin, 2017. "Practical failure recognition model of lithium-ion batteries based on partial charging process," Energy, Elsevier, vol. 138(C), pages 1199-1208.
    13. Emrouznejad, Ali & De Witte, Kristof, 2010. "COOPER-framework: A unified process for non-parametric projects," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1573-1586, December.
    14. Shaheen, Muhammad & Khan, Muhammad Zeb, 2016. "A method of data mining for selection of site for wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 1225-1233.
    15. Asil Oztekin, 2018. "Information fusion-based meta-classification predictive modeling for ETF performance," Information Systems Frontiers, Springer, vol. 20(2), pages 223-238, April.
    16. Abdorrahman Haeri, 2020. "Analyzing safety level and recognizing flaws of commercial centers through data mining approach," Journal of Risk and Reliability, , vol. 234(3), pages 512-526, June.
    17. Sebastian Büsch & Volker Nissen & Arndt Wünscher, 2017. "Automatic classification of data-warehouse-data for information lifecycle management using machine learning techniques," Information Systems Frontiers, Springer, vol. 19(5), pages 1085-1099, October.
    18. Renhe Hu & Zihan Hui & Yifan Li & Jueqi Guan, 2023. "Research on Learning Concentration Recognition with Multi-Modal Features in Virtual Reality Environments," Sustainability, MDPI, vol. 15(15), pages 1-16, July.
    19. Kazim Topuz & Hasmet Uner & Asil Oztekin & Mehmet Bayram Yildirim, 2018. "Predicting pediatric clinic no-shows: a decision analytic framework using elastic net and Bayesian belief network," Annals of Operations Research, Springer, vol. 263(1), pages 479-499, April.
    20. Mehri, Ali & Darooneh, Amir H., 2011. "The role of entropy in word ranking," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(18), pages 3157-3163.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:gam:jmathe:v:13:y:2025:i:7:p:1201-:d:1628661. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.