Flexible K Nearest Neighbors Classifier: Derivation and Application for Ion-mobility Spectrometry-based Indoor Localization

• URL: http://arxiv.org/abs/2304.10151v3
• Date: Wed, 13 Mar 2024 08:14:02 GMT
• Title: Flexible K Nearest Neighbors Classifier: Derivation and Application for Ion-mobility Spectrometry-based Indoor Localization
• Authors: Philipp M\"uller
• Abstract summary: The K Nearest Neighbors (KNN) is widely used in many fields such as fingerprint-based localization or medicine. In this paper a KNN-variant is discussed which ensures that the K nearest neighbors are indeed close to the unlabelled sample. It achieves a higher classification accuracy than the KNN in the tests, while having the same computational demand.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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