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