Extend natural neighbor: a novel classification method with self-adaptive neighborhood parameters in different stages

TL;DR

This paper introduces the extend natural neighbor (ENaN) classification method that adaptively determines neighborhood parameters at different stages, improving classification accuracy without manual parameter tuning.

Contribution

The novel ENaN method automatically predicts neighborhood size at each stage, overcoming the limitations of fixed-parameter KNN approaches.

Findings

ENaN achieves better classification accuracy than traditional KNN.

ENaN adaptively learns neighborhood size during training and testing.

The method reduces the need for manual parameter selection.

Abstract

Various kinds of k-nearest neighbor (KNN) based classification methods are the bases of many well-established and high-performance pattern-recognition techniques, but both of them are vulnerable to their parameter choice. Essentially, the challenge is to detect the neighborhood of various data sets, while utterly ignorant of the data characteristic. This article introduces a new supervised classification method: the extend natural neighbor (ENaN) method, and shows that it provides a better classification result without choosing the neighborhood parameter artificially. Unlike the original KNN based method which needs a prior k, the ENaNE method predicts different k in different stages. Therefore, the ENaNE method is able to learn more from flexible neighbor information both in training stage and testing stage, and provide a better classification result.