An Improved Weighted Base Classification for Optimum Weighted Nearest Neighbor Classifiers

Muhammad Abbas; Kamran Memon; Noor Ain; Ekang Ajebesone; Muhammad Usaid; Zulfiqar Bhutto

Research Article

An Improved Weighted Base Classification for Optimum Weighted Nearest Neighbor Classifiers

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@ARTICLE{10.4108/eai.13-7-2018.163339,
    author={Muhammad Abbas and Kamran Ali Memon and Noor ul Ain and Ekang Francis Ajebesone and Muhammad Usaid and Zulfiqar Ali Bhutto},
    title={An Improved Weighted Base Classification for Optimum Weighted Nearest Neighbor Classifiers},
    journal={EAI Endorsed Transactions on Scalable Information Systems},
    volume={7},
    number={27},
    publisher={EAI},
    journal_a={SIS},
    year={2020},
    month={2},
    keywords={Classification, k-Nearest Neighbor (kNN), Logistic Regression, Decision Trees, Cross-Validation, Machine-Learning (ML), SVM, random forest, improved version of k-nearest neighbor (IVkNN), and Python},
    doi={10.4108/eai.13-7-2018.163339}
}

Muhammad Abbas
Kamran Ali Memon
Noor ul Ain
Ekang Francis Ajebesone
Muhammad Usaid
Zulfiqar Ali Bhutto
Year: 2020
An Improved Weighted Base Classification for Optimum Weighted Nearest Neighbor Classifiers
SIS
EAI
DOI: 10.4108/eai.13-7-2018.163339

Muhammad Abbas¹, Kamran Ali Memon²^,*, Noor ul Ain³, Ekang Francis Ajebesone³, Muhammad Usaid⁴, Zulfiqar Ali Bhutto⁵

1: School of Computer Science, Beijing University of Posts and Telecommunications Beijing, China
2: School of Electronic Engineering, Beijing University of Posts & Telecommunications, Beijing, China
3: School of Information & Communication Engineering, Beijing University of Posts & Telecommunications, Beijing, China
4: Department of Electrical Engineering, Mehran University of Engineering & Technology, Jamshoro, Pakistan
5: Dawood University of Engineering and Technology, Karachi, Pakistan

*Contact email: Ali.kamran77@gmail.com

Abstract

Existing classification studies use two non-parametric classifiers- k-nearest neighbours (kNN) and decision trees, and one parametric classifier-logistic regression, generating high accuracies. Previous research work has compared the results of these classifiers with training patterns of different sizes to study alcohol tests. In this paper, the Improved Version of the kNN (IVkNN) algorithm is presented which overcomes the limitation of the conventional kNN algorithm to classify wine quality. The proposed method typically identifies the same number of nearest neighbours for each test example. Results indicate a higher Overall Accuracy (OA) that oscillates between 67% and 76%. Among the three classifiers, the least sensitive to the training sample size was the kNN and produced the unrivalled OA, followed by sequential decision trees and logistic regression. Based on the sample size, the proposed IVkNN model presented 80% accuracy and 0.375 root mean square error (RMSE).

Keywords: Classification, k-Nearest Neighbor (kNN), Logistic Regression, Decision Trees, Cross-Validation, Machine-Learning (ML), SVM, random forest, improved version of k-nearest neighbor (IVkNN), and Python

Received: 2019-12-22
Accepted: 2020-02-26
Published: 2020-02-27
Publisher: EAI

: http://dx.doi.org/10.4108/eai.13-7-2018.163339

Copyright © 2020 Muhammad Abbas et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited