Package

weka.classifiers.misc

Synopsis

This class is an implementation of the Ordinal Stochastic Dominance Learner.
Further information regarding the OSDL-algorithm can be found in:

S. Lievens, B. De Baets, K. Cao-Van (2006). A Probabilistic Framework for the Design of Instance-Based Supervised Ranking Algorithms in an Ordinal Setting. Annals of Operations Research..

Kim Cao-Van (2003). Supervised ranking: from semantics to algorithms.

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

For more information about supervised ranking, see

http://users.ugent.be/~slievens/supervised_ranking.php

Options

The table below describes the options available for OSDL.

Option	Description
balanced	If true, the balanced version of the OSDL-algorithm is used This means that distinction is made between the normal and reversed preference situation.
classificationType	Sets the way in which a single label will be extracted from the estimated distribution.
debug	If set to true, classifier may output additional info to the console.
interpolationParameter	Sets the value of the interpolation parameter s;Estimated distribution is s * f_min + (1 - s) * f_max.
interpolationParameterLowerBound	Sets the lower bound for the interpolation parameter tuning (0 <= x < 1).
interpolationParameterUpperBound	Sets the upper bound for the interpolation parameter tuning (0 < x <= 1).
numberOfPartsForInterpolationParameter	Sets the granularity for tuning the interpolation parameter; For instance if the value is 32 then 33 values for the interpolation are checked.
tuneInterpolationParameter	Whether to tune the interpolation parameter based on the bounds.
weighted	If true, the weighted version of the OSDL-algorithm is used

Capabilities

The table below describes the capabilites of OSDL.

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

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