I have a knn classifier that finds the k nearest neighbors of the given data. While classification I am not able to handle ties. I want to handle ties in the following way:

If there is only one class and it is correct class accuracy is 1

If there is only one class and it is incorrect class accuracy is 0

If there is more than one class tied for best and the correct class is one of those then accuracy = 1/no_of_classes_tied_for_best.

How do I do this in Matlab?

Code till now: test has my knnclassify results for 5 neighbors

classes_test has my verification class.

for i = 1:size(test,1)
   set = 0;
   for j = 1:size(test,2)
      if test(i,j) == classes_test(i,1) && set~=1
          set = 1;
          accuracy = accuracy + 1;
  • $\begingroup$ I don't see how your plan handles ties. Maybe you can edit the post and make it more clear. $\endgroup$ – Michael Chernick Apr 14 '17 at 18:05
  • $\begingroup$ That is what I am not able to figure out $\endgroup$ – Kobe00992 Apr 14 '17 at 18:35

When using kNN, $k$ should always be set to odd numbers in order to avoid ties (3,5,7,9,11...). This is standard knowledge in machine learning.

  • $\begingroup$ Even when using k = 3, I get ties with data. I have instances where the 3 nearest neighbors are from 3 distinct classes. and when k = 5, I got neighbors with 2 distinct classes. Ex: [1 2 3] and [1 2 2 3 3] are the classes of 3 and 5 nearest neighbors respectively. $\endgroup$ – Kobe00992 Apr 17 '17 at 4:43

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