Flexible K Nearest Neighbors Classifier: Derivation and Application for Ion-mobility Spectrometry-based Indoor Localization
This incremental improvement addresses the challenge of parameter selection in KNN for applications like indoor localization, potentially enhancing accuracy without increasing computational cost.
The paper tackles the problem of selecting K in KNN classifiers by proposing a variant that ensures neighbors are close and determines K adaptively, achieving higher classification accuracy than standard KNN in tests on theoretical scenarios and indoor localization with ion-mobility spectrometry fingerprints, with the same computational demand.
The K Nearest Neighbors (KNN) classifier is widely used in many fields such as fingerprint-based localization or medicine. It determines the class membership of unlabelled sample based on the class memberships of the K labelled samples, the so-called nearest neighbors, that are closest to the unlabelled sample. The choice of K has been the topic of various studies and proposed KNN-variants. Yet no variant has been proven to outperform all other variants. In this paper a KNN-variant is discussed which ensures that the K nearest neighbors are indeed close to the unlabelled sample and finds K along the way. The algorithm is tested and compared to the standard KNN in theoretical scenarios and for indoor localization based on ion-mobility spectrometry fingerprints. It achieves a higher classification accuracy than the KNN in the tests, while having the same computational demand.