Fuzzy sets were introduced in 1965 by Zadeh as a novel way of representing vagueness. This theory provides a tool to describe the characteristics of a too complex or ill-defined system to admit precise mathematical analysis. Most of the time, the logic of human reasoning is not based on traditional two-valued or even multi-valued logic, but logic with fuzzy truths. One of the applications of fuzzy thought is in pattern recognition. “A pattern is defined by the common denominator among the multiple instances of an entity” .
The techniques of pattern recognition are usually applied for situations which are inherently vague and uncertain [2-4]. Such situations arise when the information regarding the prototypes is “linguistic” and is based on the opinions and judgments of human experts. Examples of such situations are: handwritten character recognition, fingerprint recognition, human face recognition, classification of X-ray images, medical diagnosis and classification of remotely sensed data.
This paper addresses four new distance measures as tools in pattern recognition for intuitionistic fuzzy sets (IFSs). Following the discussion on literature of distance measures, proposed measurement scales and the proof of their properties would be presented. In order to show the reliability of addressed formulations, this paper employs them in a part of medical diagnosis progress in bacillus colonies identification. The performed experiments test validity and reliability of the proposed models by running pattern recognition process on sixty cases of four different bacilli. Resulted outcomes by IFSs approach are compared with similar measures in regular fuzzy. Numerical comparisons reveal effectiveness of the proposed distance measurement scales and related pattern recognition progress.
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(Author: Hoda Davarzani, Mohammadreza Amiri Khorheh