MIWrapper
Package
weka.classifiers.mi
Synopsis
A simple Wrapper method for applying standard propositional learners to multi-instance data.
For more information see:
E. T. Frank, X. Xu (2003). Applying propositional learning algorithms to multi-instance data. Department of Computer Science, University of Waikato, Hamilton, NZ.
Options
The table below describes the options available for MIWrapper.
Option |
Description |
|---|---|
classifier |
The base classifier to be used. |
debug |
If set to true, classifier may output additional info to the console. |
method |
The method used for testing. |
weightMethod |
The method used for weighting the instances. |
Capabilities
The table below describes the capabilites of MIWrapper.
Capability |
Supported |
|---|---|
Class |
Missing class values, Binary class, Nominal class |
Attributes |
Unary attributes, Numeric attributes, Binary attributes, Nominal attributes, Empty nominal attributes, Missing values, String attributes, Relational attributes, Date attributes |
Other |
Only multi-Instance data |
Min # of instances |
0 |