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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