Extend natural neighbor: a novel classification method with self-adaptive neighborhood parameters in different stages
This addresses the challenge of selecting optimal neighborhood parameters in KNN-based classification for pattern recognition, though it appears incremental as it builds on existing KNN frameworks.
The authors tackled the problem of parameter sensitivity in k-nearest neighbor (KNN) classification by introducing the extend natural neighbor (ENaN) method, which adaptively predicts neighborhood parameters in different stages, resulting in improved classification performance without manual parameter tuning.
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.