use random forest classifir

You can use the folding technique to overcome overfitting problem.

See the following link:

https://en.wikipedia.org/wiki/Cross-validation_(statistics)

I have already used cross validation techniques but facing some over-fitting problem. I have used Random forest algorithm and got 100% accuracy. I have used re-sampling techniques, used randomized and re-sample functions in weka and it worked. But i want to know whats the general procedure to overcome over-fitting before training the data.

Saleh Shahbeig and Maral Kolahkaj

Overfitting means that you made your model "memorize" the training set by giving "bigger" training set. Or your testing set very much resembles to your training set.

Resampling basically selects different sets for training and testing.

if you gradually decrease the size of your training set less than the first experiment you increase the change to generalize your model.

M. Raihan Saleh Shahbeig

With weka use the cross validation better than the percentage split!

You should use cross validation to validate your model.

Thanks a lot for the answers Luis Alberto Quintero-Domínguez Sana Hamdi Mustafa Cem Kasapbaşı

The sample traning dataset must be randomized enough