Package

weka.classifiers.mi

Synopsis

EMDD model builds heavily upon Dietterich's Diverse Density (DD) algorithm.
It is a general framework for MI learning of converting the MI problem to a single-instance setting using EM. In this implementation, we use most-likely cause DD model and only use 3 random selected postive bags as initial starting points of EM.

For more information see:

Qi Zhang, Sally A. Goldman: EM-DD: An Improved Multiple-Instance Learning Technique. In: Advances in Neural Information Processing Systems 14, 1073-108, 2001.

Options

The table below describes the options available for MIEMDD.

Option	Description
debug	If set to true, classifier may output additional info to the console.
filterType	The filter type for transforming the training data.
seed	The random number seed to be used.

Capabilities

The table below describes the capabilites of MIEMDD.

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

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