Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Combining Minkowski and Chebyshev: New distance proposal and survey of
  distance metrics using k-nearest neighbours classifier   This work proposes a distance that combines Minkowski and Chebyshev distances
and can be seen as an intermediary distance. This combination not only achieves
efficient run times in neighbourhood iteration tasks in Z^2, but also obtains
good accuracies when coupled with the k-Nearest Neighbours (k-NN) classifier.
The proposed distance is approximately 1.3 times faster than Manhattan distance
and 329.5 times faster than Euclidean distance in discrete neighbourhood
iterations. An accuracy analysis of the k-NN classifier using a total of 33
datasets from the UCI repository, 15 distances and values assigned to k that
vary from 1 to 200 is presented. In this experiment, the proposed distance
obtained accuracies that were better than the average more often than its
counterparts (in 26 cases out of 33), and also obtained the best accuracy more
frequently (in 9 out of 33 cases).

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The categories of this paper are: cond-mat
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The categories of this paper are: cmp-lg
10
The categories of this paper are: q-bio
27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
28
The categories of this paper are: alg-geom
22
The categories of this paper are: chao-dyn
33
The categories of this paper are: plasm-ph
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The categories of this paper are: eess
12
The categories of this paper are: nucl-th
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The categories of this paper are: adap-org
34
The categories of this paper are: chem-ph
32
The categories of this paper are: atom-ph
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The categories of this paper are: physics
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The categories of this paper are: hep-ex
30
The categories of this paper are: q-alg