Package

weka.classifiers.misc

Synopsis

This class is an implementation of the Ordinal Learning Method
Further information regarding the algorithm and variants can be found in:

Arie Ben-David (1992). Automatic Generation of Symbolic Multiattribute Ordinal Knowledge-Based DSSs: methodology and Applications. Decision Sciences. 23:1357-1372.

Lievens, Stijn (2003-2004). Studie en implementatie van instantie-gebaseerde algoritmen voor gesuperviseerd rangschikken..

Options

The table below describes the options available for OLM.

Option	Description
averagingType	Choses the way in which the distributions are averaged in the first phase of the algorithm.
classificationType	Sets the classification type.
debug	If set to true, classifier may output additional info to the console.
distanceType	Sets the distance that is to be used by the nearest neighbour rule
extensionType	Sets the extension type to use.
seed	Sets the seed that is used to randomize the instances prior to building the rule bases
sort	If true, the instances are also sorted within the classes prior to building the rule bases.

Capabilities

The table below describes the capabilites of OLM.

Capability	Supported
Class	Missing class values, Binary class, Nominal class
Attributes	Nominal attributes, Unary attributes, Binary attributes, Empty nominal attributes
Min # of instances	0