Multicriteria fuzzy classification procedure PROCFTN: Methodology and medical application

Nabil Belacel*, Mohamed Rachid Boulassel

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

29 Citations (Scopus)


In this paper, we introduce a new classification procedure for assigning objects to predefined classes, named PROCFTN. This procedure is based on a fuzzy scoring function for choosing a subset of prototypes, which represent the closest resemblance with an object to be assigned. It then applies the majority-voting rule to assign an object to a class. We also present a medical application of this procedure as an aid to assist the diagnosis of central nervous system tumours. The results are compared with those obtained by other classification methods, reported on the same data set, including decision tree, production rules, neural network, k nearest neighbor, multilayer perceptron and logistic regression. Our results are very encouraging and show that the multicriteria decision analysis approach can be successfully used to help medical diagnosis. Crown

Original languageEnglish
Pages (from-to)203-217
Number of pages15
JournalFuzzy Sets and Systems
Issue number2
Publication statusPublished - Jan 16 2004
Externally publishedYes


  • Astrocytic tumour
  • Classification
  • Fuzzy binary relations
  • Fuzzy sets
  • Medical diagnosis
  • Multicriteria decision aid
  • Scoring function

ASJC Scopus subject areas

  • Logic
  • Artificial Intelligence


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