Affiliations: School of Economics and Management, Nanjing University of Information Science and Technology, Nanjing, People's Republic of China | School of Accounting, Nanjing Audit University, Nanjing, People's Republic of China
Note: [] Corresponding author. Cai Mei, School of Economics and Management, Nanjing University of Information Science and Technology, 210044 Nanjing, People's Republic of China. Tel.: +86 13851949576; E-mail: [email protected]
Abstract: Pattern recognition (PR) can roughly be defined as an art of assigning to individual objects proper names of patterns consisting of similar to them in an assumed sense objects. Distance measure is a key concept for all attempts to deal with pattern recognition. This paper addresses the issue of linguistic ordered weighted distance measure for PR. The method enables the handling of imprecise information given by linguistic variables. Firstly we construct a non-linear programming model to identify weights of attributes based on the principle that the relative weights of positions should maximize deviations of unknown objects, in order to distinguish the objects as far as possible. Then we utilize the LOWD operator to aggregate the global evaluation of an unknown object and classify it to the pattern with the shortest distance. Finally, a numerical example is used to show the process and effects of our new proposed method.
Keywords: Pattern recognition (PR), ordered weighted distance measure, maximizing deviation method, linguistic symbolic computational model
