  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.
