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,
Nicolás.


2013/3/31 Prof. Dr. Matthias Kohl <Matthias.Kohl@stamats.de>

> 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( svm, rna, knn) I
>> need to "oversampling" the number of instances of each class.
>>
>> I have heard that there is a method to do this. It consists in generating
>> these new instances as follows:
>>
>> new_instance <---- original_instance + u(epsilon)
>>
>>   U(epsilon) is a uniform number in the range [-epsilon,epsilon] and this
>> number is applied to each feature of the dataset to obtain a new instance
>> without modified the original class.
>>
>> Anybody has used this method to "oversampling" his data? Anybody has more
>> information about it?
>>
>> Thanks in advance!
>>
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>>
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>>
> --
> Prof. Dr. Matthias Kohl
> www.stamats.de
>

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