Thanks for the answer, but I think this is not suitable for me, because as
it appears in the example of "SMOTE" function, this methods is applicable
having only two classes ( common/rare, yes/no, disease/no disease).
However, in my data, I have four classes.
If I am wrong, please, tell me.
Thanks,
see function SMOTE in package DMwR
hth
Matthias
On 31.03.2013 10:46, Nicolás Sánchez wrote:
I have a question about data mining. I have a dataset of 70 instances with
14 features that belong to 4 classes. As the number of each class is not
enough to obtain a good accuracy using some classifiers(
I have a question about data mining. I have a dataset of 70 instances with
14 features that belong to 4 classes. As the number of each class is not
enough to obtain a good accuracy using some classifiers( svm, rna, knn) I
need to "oversampling" the number of instances of each class.
I have heard t
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