A Game Theoretic Based K-Nearest Neighbor Approach for Binary Classification (2023)

Abstract

K-nearest neighbor is one of the simplest and most intuitive binary classification methods providing robust results on a wide range of data. However, classification results can be improved by using a decision method that is capable of assigning, if necessary, the minority label from the list of neighbors of a tested instance. In this paper, we propose using a simple game-theoretic model to assign labels based on the neighbors' information to enhance its performance for binary classification.

Citare

@Inproceedings{Lung2023AGT,
 author = {R. Lung and M. Suciu},
 booktitle = {IEEE Symposium Series on Computational Intelligence},
 title = {A Game Theoretic Based K-Nearest Neighbor Approach for Binary Classification},
 year = {2023}
}