Package

weka.classifiers.mi

Synopsis

Implements Stuart Andrews' mi_SVM (Maximum pattern Margin Formulation of MIL). Applying weka.classifiers.functions.SMO to solve multiple instances problem.
The algorithm first assign the bag label to each instance in the bag as its initial class label. After that applying SMO to compute SVM solution for all instances in positive bags And then reassign the class label of each instance in the positive bag according to the SVM result Keep on iteration until labels do not change anymore.

For more information see:

Stuart Andrews, Ioannis Tsochantaridis, Thomas Hofmann: Support Vector Machines for Multiple-Instance Learning. In: Advances in Neural Information Processing Systems 15, 561-568, 2003.

Options

The table below describes the options available for MISVM.

Option	Description
c	The value for C.
debug	If set to true, classifier may output additional info to the console.
filterType	The filter type for transforming the training data.
kernel	The kernel to use.
maxIterations	The maximum number of iterations to perform.

Capabilities

The table below describes the capabilites of MISVM.

Capability	Supported
Class	Missing class values, Binary class
Attributes	Unary attributes, Nominal attributes, Relational attributes, Binary attributes, Empty nominal attributes, Missing values
Other	Only multi-Instance data
Min # of instances	1

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