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

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