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Using Proximity Graph Cut for Fast and Robust Instance-Based Classification in Large Datasets
2021
Complexity
K-nearest neighbours (kNN) is a very popular instance-based classifier due to its simplicity and good empirical performance. However, large-scale datasets are a big problem for building fast and compact neighbourhood-based classifiers. This work presents the design and implementation of a classification algorithm with index data structures, which would allow us to build fast and scalable solutions for large multidimensional datasets. We propose a novel approach that uses navigable small-world
doi:10.1155/2021/2011738
doaj:2b877f8528d84394ba3f6fd5d2776363
fatcat:3pfkluar6zdvto5gsoehfwqnpq