An Improved Weighted Base Classification for Optimum Weighted Nearest Neighbor Classifiers

Authors

  • Muhammad Abbas Beijing University of Posts and Telecommunications image/svg+xml
  • Kamran Ali Memon Beijing University of Posts and Telecommunications image/svg+xml
  • Noor ul Ain Beijing University of Posts and Telecommunications image/svg+xml
  • Ekang Francis Ajebesone Beijing University of Posts and Telecommunications image/svg+xml
  • Muhammad Usaid Mehran University of Engineering and Technology image/svg+xml
  • Zulfiqar Ali Bhutto Dawood University of Engineering and Technology image/svg+xml

DOI:

https://doi.org/10.4108/eai.13-7-2018.163339

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), Python

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).

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Published

27-02-2020

How to Cite

1.
Abbas M, Memon KA, ul Ain N, Francis Ajebesone E, Usaid M, Bhutto ZA. An Improved Weighted Base Classification for Optimum Weighted Nearest Neighbor Classifiers. EAI Endorsed Scal Inf Syst [Internet]. 2020 Feb. 27 [cited 2024 Nov. 22];7(27):e1. Available from: https://publications.eai.eu/index.php/sis/article/view/2102