We implement two different types of classifier:

• Naive Bayesian Classifier (NBC)
• Decision Tree Classifier (DTC)

• f The name of the data file
• m Max number of different values per feature
• n Number of NBC and DTC
• p The percentage of the data set to be used for training
• d The maximum depth for the DTCs [Default: 0 which means maximum possible depth]
• k Keep losers

If you want to work with the page-blocks dataset, use 10 NBC and 10 DTC(with a max depth of 2) and split the use 80% of the dataset as a training set and the other 20% as a test set, type de following parameters:

-f datasets/page-blocks.txt -m 50 -n 10 0 -p 80 -d 2

For dataset:

• "page-blocks" use m = 75
• "glass" use m = 15
• "pen-digits" use m = 100
• "yeast" use m = 100
• "optdigits" use m = 16