A Pattern Classification Model for Vowel Data Using Fuzzy Nearest Neighbor  

作　　者：Monika Khandelwal Ranjeet Kumar Rout Saiyed Umer Kshira Sagar Sahoo NZ Jhanjhi Mohammad Shorfuzzaman Mehedi Masud 

机构地区：[1]Department of Computer Science and Engineering,National Institute of Technology Srinagar,Hazratbal,190006,Jammu and Kashmir,India [2]Department of Computer Science and Engineering,Aliah University,Kolkata,India [3]Department of Computer Science and Engineering,SRM University,Amaravati,522240,AP,India [4]School of Computer Science SCS,Taylor’s University,Subang Jaya,47500,Malaysia [5]Department of Computer Science,College of Computers and Information Technology,Taif University,P.O.Box 11099,Taif,21944,Saudi Arabia

出　　处：《Intelligent Automation & Soft Computing》2023年第3期3587-3598,共12页智能自动化与软计算（英文）

基　　金：supported by the Taif University Researchers Supporting Project Number(TURSP-2020/79),Taif University,Taif,Saudi Arabia.

摘　　要：Classification of the patterns is a crucial structure of research and applications. Using fuzzy set theory, classifying the patterns has become of great interest because of its ability to understand the parameters. One of the problemsobserved in the fuzzification of an unknown pattern is that importance is givenonly to the known patterns but not to their features. In contrast, features of thepatterns play an essential role when their respective patterns overlap. In this paper,an optimal fuzzy nearest neighbor model has been introduced in which a fuzzifi-cation process has been carried out for the unknown pattern using k nearest neighbor. With the help of the fuzzification process, the membership matrix has beenformed. In this membership matrix, fuzzification has been carried out of the features of the unknown pattern. Classification results are verified on a completelyllabelled Telugu vowel data set, and the accuracy is compared with the differentmodels and the fuzzy k nearest neighbor algorithm. The proposed model gives84.86% accuracy on 50% training data set and 89.35% accuracy on 80% trainingdata set. The proposed classifier learns well enough with a small amount of training data, resulting in an efficient and faster approach.

关 键 词：Nearest neighbors fuzzy classification patterns recognition reasoning rule membership matrix 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]