Classification by voting feature intervals. Intervals are constucted around each class for each attribute (basically discretization). Class counts are recorded for each interval on each attribute. Classification is by voting. For more info see: G. Demiroz, A. Guvenir: Classification by voting feature intervals. In: 9th European Conference on Machine Learning, 85-92, 1997. Have added a simple attribute weighting scheme. Higher weight is assigned to more confident intervals, where confidence is a function of entropy: weight (att_i) = (entropy of class distrib att_i / max uncertainty)^-bias
(based on WEKA 3.6)
For further options, click the 'More' - button in the dialog.
All weka dialogs have a panel where you can specify classifier-specific parameters.