I have a dataset that has 2 classes: normal and attack.
In order to model a behaviour-based IDS, I want to split the dataset into
training and test sets, but I want that the training set only has instances
with class normal; the test set will contain the remaining instances.
1) Is this possible with Weka?
2) How can I do that using the Weka Java API?
Thank you very much for your help,
I have problems explaining exactly how nested cross validation is performed
when using AttributeSelectedClassifier in Weka. I have used the
AttributeSelectedClassifier with a wrapper and the same classifier (Random
Forest) in both stages. From the beginning - what exactly happens during
each step of the procedure? When is feature selected and when are the RF
I need to understand this thoroughly and hope for help :-)
Have a nice day,
Recent threads have mentioned that Weka is using the Zulu JRE in it’s latest versions.
Why was this chosen over openjdk Oracle jre’s?
If weka.jar is used in a java application using a openjdk/Oracle JRE are there any potential compatibility issues?
I am trying to classify date attributes, but I have encountered a new problem. After having discovered a problem with a package that crashed Weka, now the date attribute does not order the values correctly. Is it because there are "empty / unknown" values with a symbol "?" in the dataset? Together I send an image and the dataset for analysis.
How can we handle the outliers using experimenter? I know there is a way to
exclude them using InterquartileRange but I am interested in dealing with
other ways rather than deleting them, such as replacing them with other