Weighted Instance Typicality Search (WITS): A nearest neighbor data reduction algorithm

@article{Morring2004WeightedIT,
  title={Weighted Instance Typicality Search (WITS): A nearest neighbor data reduction algorithm},
  author={Brent D. Morring and Tony R. Martinez},
  journal={Intell. Data Anal.},
  year={2004},
  volume={8},
  pages={61-78}
}
Two disadvantages of the standard nearest neighbor algorithm are 1) it must store all the instances of the training set, thus creating a large memory footprint and 2) it must search all the instances of the training set to predict the classification of a new query point, thus it is slow at run time. Much work has been done to remedy these shortcomings. This paper presents a new algorithm WITS (Weighted-Instance Typicality Search) and a modified version, Clustered-WITS (C-WITS), designed to… CONTINUE READING
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