IDEAS home Printed from https://ideas.repec.org/h/spr/isochp/978-3-319-65455-3_13.html
   My bibliography  Save this book chapter

Classification of Cancer Data: Analyzing Gene Expression Data Using a Fuzzy Decision Tree Algorithm

In: Operations Research Applications in Health Care Management

Author

Listed:
  • Simone A. Ludwig

    (North Dakota State University)

  • Stjepan Picek

    (KU Leuven, ESAT/COSIC and iMinds)

  • Domagoj Jakobovic

    (University of Zagreb)

Abstract

Decision tree algorithms are very popular in the area of data mining since the algorithms have a simple inference mechanism and provide a comprehensible way to represent the model. Over the past years, fuzzy decision tree algorithms have been proposed in order to handle the uncertainty in the data. Fuzzy decision tree algorithms have shown to outperform classical decision tree algorithms. This chapter investigates a fuzzy decision tree algorithm applied to the classification of gene expression data. The fuzzy decision tree algorithm is compared to a classical decision tree algorithm as well as other well-known data mining algorithms commonly applied to classification tasks. Based on the five data sets analyzed, the fuzzy decision tree algorithm outperforms the classical decision tree algorithm. However, compared to other commonly used classification algorithms, both decision tree algorithms are competitive, but they do not reach the accuracy values of the best performing classifier. One of the advantages of decision tree models including the fuzzy decision tree algorithm is however the simplicity and comprehensibility of the model as demonstrated in the chapter.

Suggested Citation

  • Simone A. Ludwig & Stjepan Picek & Domagoj Jakobovic, 2018. "Classification of Cancer Data: Analyzing Gene Expression Data Using a Fuzzy Decision Tree Algorithm," International Series in Operations Research & Management Science, in: Cengiz Kahraman & Y. Ilker Topcu (ed.), Operations Research Applications in Health Care Management, chapter 0, pages 327-347, Springer.
    • Handle: RePEc:spr:isochp:978-3-319-65455-3_13
    DOI: 10.1007/978-3-319-65455-3_13
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    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:isochp:978-3-319-65455-3_13. 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: 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.