VFI
Package
weka.classifiers.misc
Synopsis
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
Options
The table below describes the options available for VFI.
Option |
Description |
|---|---|
bias |
Strength of bias towards more confident features |
debug |
If set to true, classifier may output additional info to the console. |
weightByConfidence |
Weight feature intervals by confidence |
Capabilities
The table below describes the capabilites of VFI.
Capability |
Supported |
|---|---|
Class |
Missing class values, Binary class, Nominal class |
Attributes |
Unary attributes, Empty nominal attributes, Nominal attributes, Numeric attributes, Binary attributes, String attributes, Missing values, Date attributes |
Min # of instances |
0 |