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 |