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Husky
0

classification experiments with the k-Nearest Neighbor algorithm.

Asked by Husky
on 5 Feb 2013

Hi everyone

It's my first effort for KNN classification and am using wine data sample from UCI website. i was wondering what distance measures I can use in this case and how to get the computation time required to compute the distance from a sample to each other sample.

I want to determine its k nearest neighbors for each labeled sample, if the label of the sample is more common than any other label for the k nearest neighbors, then count the classification as correct. Otherwise, count the classification as incorrect.

I'd appreciate any input

Thanks

data set info: attributes :
      1) Alcohol
	2) Malic acid
	3) Ash
	4) Alcalinity of ash  
and ... 13)xxx

it categorizes wines into 3 classes

one example would be:

1,14.23,1.71,2.43,15.6,127,2.8,3.06,.28,2.29,5.64,1.04,3.92,1065

  2 Comments

Measuring computation time is difficult to do right, especially if what you are trying to do is predict how efficient different methods would be with larger datasets.

Husky
on 5 Feb 2013

Thanks walter, this dataset is small, only 178 lines each having 13 attributes

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1 Answer

Answer by Walter Roberson
on 5 Feb 2013

For timing, see the File Exchange Contribution timeit

  1 Comment

Husky
on 5 Feb 2013

Thanks but how exactly should I use this function in KNN code?


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