I have heard that sometimes the more we trained the dataset the better the performance. I really do not know other way than downloading the dataset and choose the classifier and press the button "Start"; then, we get the result. Is there any other way to train the dataset more in WEKA using the GUI?
In an IterativeClassifier, the number of iterations determines how long the classifier is trained (e.g., how many base classifiers are added to the ensemble classifier). You can use the IterativeClassifierOptimizer to automatically select an appropriate number of iterations using internal cross-validation.
In an UpdateableClassifier, you will need to write some code or script to run the training data through the learning algorithm multiple times. I don’t think there is an automatic way to do this in WEKA (but I might be wrong, perhaps it can be done with the KnowledgeFlow).
Overfitting is obviously an issue in both cases, so internal cross-validation (or similar) should be used to decide when to strop training.