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Extensions of the \(k\) nearest neighbour methods for classification problems. (English) Zbl 1157.68448
Gammerman, A. (ed.), Artificial intelligence and applications. Machine learning. As part of the 26th IASTED international multi-conference on applied informatics. Calgary: International Association of Science and Technology for Development (IASTED); Anaheim, CA: Acta Press (ISBN 978-0-88986-710-9/CD-ROM). 23-28 (2008).
Summary: The \(k\) Nearest Neighbour (kNN) method is a widely used technique which has found several applications in clustering and classification. In this paper, we focus on classification problems and we propose modifications of the nearest neighbour method that exploit information from the structure of a dataset. The results of our experiments using datasets from the UCI repository demonstrate that the classifiers produced perform generally better than the classic kNN and are more reliable, without being significantly slower.
For the entire collection see [Zbl 1154.68012].
MSC:
68T10 Pattern recognition, speech recognition
Software:
UCI-ml
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