An Integrated Intuitionistic Fuzzy Similarity Measures for Medical Problems

DOI

10.1080/18756891.2013.856259How to use a DOI?

Keywords

Intuitionistic fuzzy sets, similarity measure, pattern recognition, medical diagnosis

Abstract

The purpose of this paper is to develop an integrated similarity measures model based on intuitionistic fuzzy sets. This integrated model has improved two similarity measures methods: (1) Ye (, 53, 91-97 (2011)) presented a novel cosine similarity measures method for handling pattern recognition problems based on intuitionistic fuzzy sets. However, in some cases, Ye's method can not give sufficient information to discriminate a sample between two patterns. Therefore, we provide an improved method for the similarity measure. (2) Hung et al. (, 40, 447-454 (2008)) provided a new score function to measure the degree of suitability of each alternative. In this paper, we extend their method to modify the hesitation parameter with rate operations as a defuzzfication function for each characteristic, and then the defuzzy results as a parameter input new similarity measure method based on the Minkowski distance conception. Finally, the proposed similarity measures model is applied to two medical diagnosis problems to demonstrate the usefulness of this study. Furthermore, in order to make computing and ranking results easier, a computer-based interface system is also developed, and this system may help to make a decision more efficiently.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Kuo-Chen Hung
AU  - Pei-Kuang Wang
PY  - 2014
DA  - 2014/04/01
TI  - An Integrated Intuitionistic Fuzzy Similarity Measures for Medical Problems
JO  - International Journal of Computational Intelligence Systems
SP  - 327
EP  - 343
VL  - 7
IS  - 2
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2013.856259
DO  - 10.1080/18756891.2013.856259
ID  - Hung2014
ER  -