Multicriteria fuzzy classification procedure PROCFTN: Methodology and medical application

Research output: Contribution to journalArticle

23 Citations (Scopus)

Abstract

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
Volume141
Issue number2
DOIs
Publication statusPublished - Jan 16 2004

Fingerprint

Fuzzy Classification
Medical Applications
Medical applications
Multi-criteria
Methodology
Decision theory
Neurology
Multilayer neural networks
Decision trees
Set theory
Multi-criteria Decision Analysis
Logistics
Tumors
Majority Voting
Production Rules
Logistic Regression
Perceptron
Neural networks
Scoring
Decision tree

Keywords

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

ASJC Scopus subject areas

  • Logic
  • Artificial Intelligence

Cite this

Multicriteria fuzzy classification procedure PROCFTN : Methodology and medical application. / Belacel, Nabil; Boulassel, Mohamed Rachid.

In: Fuzzy Sets and Systems, Vol. 141, No. 2, 16.01.2004, p. 203-217.

Research output: Contribution to journalArticle

@article{d72c54220321464ca4914091dd458014,
title = "Multicriteria fuzzy classification procedure PROCFTN: Methodology and medical application",
abstract = "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",
keywords = "Astrocytic tumour, Classification, Fuzzy binary relations, Fuzzy sets, Medical diagnosis, Multicriteria decision aid, Scoring function",
author = "Nabil Belacel and Boulassel, {Mohamed Rachid}",
year = "2004",
month = "1",
day = "16",
doi = "10.1016/S0165-0114(03)00022-8",
language = "English",
volume = "141",
pages = "203--217",
journal = "Fuzzy Sets and Systems",
issn = "0165-0114",
publisher = "Elsevier",
number = "2",

}

TY - JOUR

T1 - Multicriteria fuzzy classification procedure PROCFTN

T2 - Methodology and medical application

AU - Belacel, Nabil

AU - Boulassel, Mohamed Rachid

PY - 2004/1/16

Y1 - 2004/1/16

N2 - 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

AB - 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

KW - Astrocytic tumour

KW - Classification

KW - Fuzzy binary relations

KW - Fuzzy sets

KW - Medical diagnosis

KW - Multicriteria decision aid

KW - Scoring function

UR - http://www.scopus.com/inward/record.url?scp=0346778554&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0346778554&partnerID=8YFLogxK

U2 - 10.1016/S0165-0114(03)00022-8

DO - 10.1016/S0165-0114(03)00022-8

M3 - Article

AN - SCOPUS:0346778554

VL - 141

SP - 203

EP - 217

JO - Fuzzy Sets and Systems

JF - Fuzzy Sets and Systems

SN - 0165-0114

IS - 2

ER -