Open Access

ARTICLE

A Pattern Classification Model for Vowel Data Using Fuzzy Nearest Neighbor

Monika Khandelwal¹, Ranjeet Kumar Rout¹, Saiyed Umer², Kshira Sagar Sahoo³, NZ Jhanjhi^4,*, Mohammad Shorfuzzaman⁵, Mehedi Masud⁵

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

* Corresponding Author: NZ Jhanjhi. Email:

Intelligent Automation & Soft Computing 2023, 35(3), 3587-3598. https://doi.org/10.32604/iasc.2023.029785

Received 11 March 2022; Accepted 09 June 2022; Issue published 17 August 2022

Abstract

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 problems observed in the fuzzification of an unknown pattern is that importance is given only to the known patterns but not to their features. In contrast, features of the patterns play an essential role when their respective patterns overlap. In this paper, an optimal fuzzy nearest neighbor model has been introduced in which a fuzzification process has been carried out for the unknown pattern using k nearest neighbor. With the help of the fuzzification process, the membership matrix has been formed. In this membership matrix, fuzzification has been carried out of the features of the unknown pattern. Classification results are verified on a completely llabelled Telugu vowel data set, and the accuracy is compared with the different models and the fuzzy k nearest neighbor algorithm. The proposed model gives 84.86% accuracy on 50% training data set and 89.35% accuracy on 80% training data set. The proposed classifier learns well enough with a small amount of training data, resulting in an efficient and faster approach.

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Keywords

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