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I want to implement k-NN to use in a multi-class dataset. I found "A k-Nearest Neighbor Based Algorithm for Multi-label Classification" but didn't get the algorithm.

Do you know any clear explanation of it?

What I have in mind it works as follows:

  1. Calculate posterior probabilities for each class (simply by dividing number of samples who are labelled as class_i to the number of total samples).

  2. Then, for each test sample find it's nearest neighbours (e.g. assume k = 5, so we find top 5 nearest neighbour among all samples in the training set).

  3. Then take majority class among these 5 neighbours as the class of the test sample (so if 3 of the 5 nearest neighbour has the Classx, then we'll classify the test sample as Classx).

Would that be a correct implementation?

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  • $\begingroup$ As bayer answered, I think you meant multi-class classification. The paper you cited talk about multi-label classification, i.e. every record has many different labels. For multi-class classification with kNN just have a look to classical Data Mining book as "Introduction to Data Mining" by Tan et al. $\endgroup$ – Simone Sep 21 '11 at 7:12
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It depends on what you mean by multi class--are you talking of a setting where (a) one item can have multiple classes or (b) an item can have one of many classes (as opposed to binary classification)?

From your proposal algorithm I take that you mean (b).

In that case, your idea is right, except that there is no need to take the prior (I think you mean prior instead of posterior probabilities in 1.) into account. That's typically not done, although I have no solid argument for that right now.

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For corret multi label classification, means several catergories in result you have tune you algorithm measuring method. So you have to evaluate set(examples) to set(predicted result) comparison and also try several tresholds(radius of sphere) in which you select all categories.

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