IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0041882.html
   My bibliography  Save this article

A Comparison of MCC and CEN Error Measures in Multi-Class Prediction

Author

Listed:
  • Giuseppe Jurman
  • Samantha Riccadonna
  • Cesare Furlanello

Abstract

We show that the Confusion Entropy, a measure of performance in multiclass problems has a strong (monotone) relation with the multiclass generalization of a classical metric, the Matthews Correlation Coefficient. Analytical results are provided for the limit cases of general no-information (n-face dice rolling) of the binary classification. Computational evidence supports the claim in the general case.

Suggested Citation

  • Giuseppe Jurman & Samantha Riccadonna & Cesare Furlanello, 2012. "A Comparison of MCC and CEN Error Measures in Multi-Class Prediction," PLOS ONE, Public Library of Science, vol. 7(8), pages 1-8, August.
    • Handle: RePEc:plo:pone00:0041882
    DOI: 10.1371/journal.pone.0041882
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0041882
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0041882&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0041882?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
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bode, Gerrit & Thul, Simon & Baranski, Marc & Müller, Dirk, 2020. "Real-world application of machine-learning-based fault detection trained with experimental data," Energy, Elsevier, vol. 198(C).
    2. Kong, Hyeongwoo & Yun, Wonje & Kim, Woo Chang, 2023. "Tracking customer risk aversion," Finance Research Letters, Elsevier, vol. 54(C).
    3. Van Quan Tran & Hai-Van Thi Mai & Thuy-Anh Nguyen & Hai-Bang Ly, 2021. "Investigation of ANN architecture for predicting the compressive strength of concrete containing GGBFS," PLOS ONE, Public Library of Science, vol. 16(12), pages 1-21, December.
    4. Sankhadeep Chatterjee & Sarbartha Sarkar & Nilanjan Dey & Soumya Sen, 2018. "Non-Dominated Sorting Genetic Algorithm-II-Induced Neural-Supported Prediction of Water Quality with Stability Analysis," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 17(02), pages 1-20, June.
    5. Nader Salari & Shamarina Shohaimi & Farid Najafi & Meenakshii Nallappan & Isthrinayagy Karishnarajah, 2014. "A Novel Hybrid Classification Model of Genetic Algorithms, Modified k-Nearest Neighbor and Developed Backpropagation Neural Network," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-50, November.
    6. Yun Jiang & Li Chen & Hai Zhang & Xiao Xiao, 2019. "Breast cancer histopathological image classification using convolutional neural networks with small SE-ResNet module," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-21, March.

    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:plo:pone00:0041882. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.