IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v82y2015icp199-206.html
   My bibliography  Save this article

Kernel multilogit algorithm for multiclass classification

Author

Listed:
  • Dalmau, Oscar
  • Alarcón, Teresa E.
  • González, Graciela

Abstract

An algorithm for multi-class classification is proposed. The soft classification problem is considered, where the target variable is a multivariate random variable. The proposed algorithm transforms the original target variable into a new space using the multilogit function. Assuming Gaussian noise on this transformation and using a standard Bayesian approach the model yields a quadratic functional whose global minimum can easily be obtained by solving a set of linear system of equations. In order to obtain the classification, the inverse multilogit-based transformation should be applied and the obtained result can be interpreted as a ‘soft’ or probabilistic classification. Then, the final classification is obtained by using the ‘Winner takes all’ strategy. A Kernel-based formulation is presented in order to consider the non-linearities associated with the feature space of the data. The proposed algorithm is applied on real data, using databases available online. The experimental study shows that the algorithm is competitive with respect to other classical algorithms for multiclass classification.

Suggested Citation

  • Dalmau, Oscar & Alarcón, Teresa E. & González, Graciela, 2015. "Kernel multilogit algorithm for multiclass classification," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 199-206.
    • Handle: RePEc:eee:csdana:v:82:y:2015:i:c:p:199-206
    DOI: 10.1016/j.csda.2014.09.007
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947314002679
    Download Restriction: Full text for ScienceDirect subscribers only.
    File URL: https://libkey.io/10.1016/j.csda.2014.09.007?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. Dankmar Böhning, 1992. "Multinomial logistic regression algorithm," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 44(1), pages 197-200, March.
    2. Caudill, Steven B, 1988. "An Advantage of the Linear Probability Model over Probit or Logit," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 50(4), pages 425-427, November.
    3. Gray, J. Brian & Fan, Guangzhe, 2008. "Classification tree analysis using TARGET," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1362-1372, January.
    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. Matteo Migheli, 2021. "Green purchasing: the effect of parenthood and gender," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(7), pages 10576-10600, July.
    2. Lucian Belascu & Alexandra Horobet & Georgiana Vrinceanu & Consuela Popescu, 2021. "Performance Dissimilarities in European Union Manufacturing: The Effect of Ownership and Technological Intensity," Sustainability, MDPI, vol. 13(18), pages 1-19, September.
    3. Antoine Marsaudon & Josselin Thuilliez, 2016. "Does democracy reduce the HIV epidemic? Evidence from Kenya," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01400833, HAL.
    4. Melissa Newham & Jo Seldeslachts & Albert Banal-Estanol, 2018. "Common ownership and market entry: Evidence from the pharmaceutical industry," Working Papers of Department of Management, Strategy and Innovation, Leuven 623896, KU Leuven, Faculty of Economics and Business (FEB), Department of Management, Strategy and Innovation, Leuven.
    5. Adrian C. Darnell, 1994. "A Dictionary Of Econometrics," Books, Edward Elgar Publishing, number 118.
    6. Jaka Cepec & Peter Grajzl & Barbara Mörec, 2022. "Public cash and modes of firm exit," Journal of Evolutionary Economics, Springer, vol. 32(1), pages 247-298, January.
    7. Alexandra Horobet & Stefania Cristina Curea & Alexandra Smedoiu Popoviciu & Cosmin-Alin Botoroga & Lucian Belascu & Dan Gabriel Dumitrescu, 2021. "Solvency Risk and Corporate Performance: A Case Study on European Retailers," JRFM, MDPI, vol. 14(11), pages 1-34, November.
    8. Mekdim D. Regassa & Mohammed B. Degnet & Mequanint B. Melesse, 2023. "Access to credit and heterogeneous effects on agricultural technology adoption: Evidence from large rural surveys in Ethiopia," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 71(2), pages 231-253, June.
    9. Clifford Afoakwah & Isaac Koomson, 2021. "How does school travel time impact children’s learning outcomes in a developing country?," Review of Economics of the Household, Springer, vol. 19(4), pages 1077-1097, December.
    10. Sleeper, Colin & Cartwright, Kate & van der Goes, David N., 2023. "The relationship between mental health and public attention to the Brett Kavanaugh hearings and confirmation," Social Science & Medicine, Elsevier, vol. 335(C).
    11. Steven F. Lehrer & Tian Xie, 2022. "The Bigger Picture: Combining Econometrics with Analytics Improves Forecasts of Movie Success," Management Science, INFORMS, vol. 68(1), pages 189-210, January.
    12. Hyunbae Chun & Jung Hur & Doyoung Kim & Nyeong Seon Son, 2020. "Cross‐Border Vertical Integration and Technology in Factory Asia: Evidence from Korea," The Developing Economies, Institute of Developing Economies, vol. 58(2), pages 99-133, June.
    13. Tian, Guo-Liang & Tang, Man-Lai & Liu, Chunling, 2012. "Accelerating the quadratic lower-bound algorithm via optimizing the shrinkage parameter," Computational Statistics & Data Analysis, Elsevier, vol. 56(2), pages 255-265.
    14. Menon, Nidhiya, 2021. "Does BMI predict the early spatial variation and intensity of Covid-19 in developing countries? Evidence from India," Economics & Human Biology, Elsevier, vol. 41(C).
    15. Claudia Schmiedeberg & Nina Schumann, 2019. "Poverty and Adverse Peer Relationships among Children in Germany: a Longitudinal Study," Child Indicators Research, Springer;The International Society of Child Indicators (ISCI), vol. 12(5), pages 1717-1733, October.
    16. Zou, Baoling & Mishra, Ashok K. & Luo, Biliang, 2020. "Grain subsidy, off-farm labor supply and farmland leasing: Evidence from China," China Economic Review, Elsevier, vol. 62(C).
    17. Walied Keshk & Hung‐Yuan (Richard) Lu & Vivek Mande, 2020. "How have US banks adopted the Financial Accounting Standards Board's Level 3 fair value disclosure rules?," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 60(S1), pages 693-727, April.
    18. Regassa, Mekdim D. & Esenaliev, Damir & Tzvetkova, Milena & Baliki, Ghassan & Schreiner, Monika & Stojetz, Wolfgang & Brück, Tilman, 2024. "The impacts of exposure to COVID-19 on food security and diet diversity in Africa," IAAE 2024 Conference, August 2-7, 2024, New Delhi, India 344321, International Association of Agricultural Economists (IAAE).
    19. Bohning, Dankmar, 1999. "The lower bound method in probit regression," Computational Statistics & Data Analysis, Elsevier, vol. 30(1), pages 13-17, March.
    20. Ulrich Nguemdjo & Bruno Ventelou, 2020. "How do migrations affect under-five mortality in rural areas? Evidence from Niakhar, Senegal," Working Papers halshs-03078776, HAL.

    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:82:y:2015:i:c:p:199-206. 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.