k-Nearest Neighbor Optimization via Randomized Hyperstructure Convex Hull

TL;DR

This paper introduces a novel k-NN optimization method using randomized hyperstructure convex hulls, improving classification accuracy by better selecting neighbors, and demonstrating competitive performance against SVMs across multiple datasets.

Contribution

The paper proposes a new neighbor selection approach in k-NN using randomized hyperstructure convex hulls, enhancing accuracy over traditional methods.

Findings

Achieved 85.71% accuracy on Haberman's dataset, outperforming the conventional 80.95%.

Achieved 94.44% accuracy on Seeds dataset, surpassing the conventional 88.89%.

Performs comparably or better than SVM on multiple datasets.

Abstract

In the k-nearest neighbor algorithm (k-NN), the determination of classes for test instances is usually performed via a majority vote system, which may ignore the similarities among data. In this research, the researcher proposes an approach to fine-tune the selection of neighbors to be passed to the majority vote system through the construction of a random n-dimensional hyperstructure around the test instance by introducing a new threshold parameter. The accuracy of the proposed k-NN algorithm is 85.71%, while the accuracy of the conventional k-NN algorithm is 80.95% when performed on the Haberman's Cancer Survival dataset, and 94.44% for the proposed k-NN algorithm, compared to the conventional's 88.89% accuracy score on the Seeds dataset. The proposed k-NN algorithm is also on par with the conventional support vector machine algorithm accuracy, even on the Banknote Authentication and Iris datasets, even surpassing the accuracy of support vector machine on the Seeds dataset.