Solving the Problem of the K Parameter in the KNN Classifier Using an Ensemble Learning Approach

TL;DR

This paper introduces an ensemble learning method to optimize the K parameter in KNN classifiers by combining multiple weak classifiers with different K values, improving performance across various real-world problems.

Contribution

The paper proposes a novel ensemble approach for selecting the K parameter in KNN, outperforming traditional methods and demonstrating broad applicability.

Findings

Outperforms traditional KNN with fixed K

Competitive with other classifiers

Shows strong potential for diverse applications

Abstract

This paper presents a new solution for choosing the K parameter in the k-nearest neighbor (KNN) algorithm, the solution depending on the idea of ensemble learning, in which a weak KNN classifier is used each time with a different K, starting from one to the square root of the size of the training set. The results of the weak classifiers are combined using the weighted sum rule. The proposed solution was tested and compared to other solutions using a group of experiments in real life problems. The experimental results show that the proposed classifier outperforms the traditional KNN classifier that uses a different number of neighbors, is competitive with other classifiers, and is a promising classifier with strong potential for a wide range of applications.