IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v47y2004i1p149-164.html

An unbiased method for constructing multilabel classification trees

Author

Listed:
  • Noh, Hyun Gon
  • Song, Moon Sup
  • Park, Sung Hyun

Abstract

No abstract is available for this item.

Suggested Citation

  • Noh, Hyun Gon & Song, Moon Sup & Park, Sung Hyun, 2004. "An unbiased method for constructing multilabel classification trees," Computational Statistics & Data Analysis, Elsevier, vol. 47(1), pages 149-164, August.
    • Handle: RePEc:eee:csdana:v:47:y:2004:i:1:p:149-164
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(03)00243-3
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Kim H. & Loh W.Y., 2001. "Classification Trees With Unbiased Multiway Splits," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 589-604, June.
    2. Siciliano, Roberta & Mola, Francesco, 2000. "Multivariate data analysis and modeling through classification and regression trees," Computational Statistics & Data Analysis, Elsevier, vol. 32(3-4), pages 285-301, January.
    3. Nettleton, Dan & Banerjee, T., 2001. "Testing the equality of distributions of random vectors with categorical components," Computational Statistics & Data Analysis, Elsevier, vol. 37(2), pages 195-208, August.
    4. D. R. Cox, 1972. "The Analysis of Multivariate Binary Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 21(2), pages 113-120, June.
    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. Lee, Tzu-Haw & Shih, Yu-Shan, 2006. "Unbiased variable selection for classification trees with multivariate responses," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 659-667, November.
    2. Strobl, Carolin & Boulesteix, Anne-Laure & Augustin, Thomas, 2007. "Unbiased split selection for classification trees based on the Gini Index," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 483-501, September.
    3. Christel Faes & Marc Aerts & Helena Geys & Geert Molenberghs, 2007. "Model Averaging Using Fractional Polynomials to Estimate a Safe Level of Exposure," Risk Analysis, John Wiley & Sons, vol. 27(1), pages 111-123, February.
    4. Brown, Timothy T. & Lee, Jadyn & Markarian, Sione, 2024. "The causal impact of shared decision making on pain outcomes: Gender matters," Social Science & Medicine, Elsevier, vol. 355(C).
    5. Bel, K. & Paap, R., 2014. "A Multivariate Model for Multinomial Choices," Econometric Institute Research Papers EI 2014-26, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    6. Honig, Benson & Samuelsson, Mikael, 2021. "Business planning by intrapreneurs and entrepreneurs under environmental uncertainty and institutional pressure," Technovation, Elsevier, vol. 99(C).
    7. Cappelli, Carmela & Mola, Francesco & Siciliano, Roberta, 2002. "A statistical approach to growing a reliable honest tree," Computational Statistics & Data Analysis, Elsevier, vol. 38(3), pages 285-299, January.
    8. Harald Hruschka, 2024. "Relevance of dynamic variables in multicategory choice models," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 46(1), pages 109-133, March.
    9. Kwak, Kyuseop & Duvvuri, Sri Devi & Russell, Gary J., 2015. "An Analysis of Assortment Choice in Grocery Retailing," Journal of Retailing, Elsevier, vol. 91(1), pages 19-33.
    10. Emilio Carrizosa & Cristina Molero-Río & Dolores Romero Morales, 2021. "Mathematical optimization in classification and regression trees," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(1), pages 5-33, April.
    11. Di Tommaso, M.L. & Weeks, M., 2000. "Decision Structures and Discrete Choices: An Application to Labour Market Participation and Fertility," Cambridge Working Papers in Economics 0009, Faculty of Economics, University of Cambridge.
    12. Kromidha, Endrit & Li, Matthew C., 2019. "Determinants of leadership in online social trading: A signaling theory perspective," Journal of Business Research, Elsevier, vol. 97(C), pages 184-197.
    13. Silke Janitza & Ender Celik & Anne-Laure Boulesteix, 2018. "A computationally fast variable importance test for random forests for high-dimensional data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(4), pages 885-915, December.
    14. Bonnet, Céline & Richards, Timothy J., 2016. "Models of Consumer Demand for Differentiated Products," TSE Working Papers 16-741, Toulouse School of Economics (TSE).
    15. George Petrakos & Claudio Conversano & Gregory Farmakis & Francesco Mola & Roberta Siciliano & Photis Stavropoulos, 2004. "New ways of specifying data edits," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 167(2), pages 249-274, May.
    16. Shih, Yu-Shan & Tsai, Hsin-Wen, 2004. "Variable selection bias in regression trees with constant fits," Computational Statistics & Data Analysis, Elsevier, vol. 45(3), pages 595-607, April.
    17. Hao Bai & Yuan Zhong & Xin Gao & Wei Xu, 2020. "Multivariate Mixed Response Model with Pairwise Composite-Likelihood Method," Stats, MDPI, vol. 3(3), pages 1-18, July.
    18. Kim, Chul & Jun, Duk Bin & Park, Sungho, 2018. "Capturing flexible correlations in multiple-discrete choice outcomes using copulas," International Journal of Research in Marketing, Elsevier, vol. 35(1), pages 34-59.
    19. Francesco Bartolucci & Claudia Pigini, 2018. "Partial effects estimation for fixed-effects logit panel data models," Working Papers 431, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    20. Pier Perri & Peter Heijden, 2012. "A Property of the CHAID Partitioning Method for Dichotomous Randomized Response Data and Categorical Predictors," Journal of Classification, Springer;The Classification Society, vol. 29(1), pages 76-90, April.

    More about this item

    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:eee:csdana:v:47:y:2004:i:1:p:149-164. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    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.