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
(based on WEKA 3.7)
For further options, click the 'More' - button in the dialog.
All weka dialogs have a panel where you can specify classifier-specific parameters.
D: If set, classifier is run in debug mode and may output additional info to the console
C: Sets the classification type to be used. (Default: MED)
B: Use the balanced version of the Ordinal Stochastic Dominance Learner
W: Use the weighted version of the Ordinal Stochastic Dominance Learner
S: Sets the value of the interpolation parameter (not with -W/T/P/L/U) (default: 0.5).
T: Tune the interpolation parameter (not with -W/S) (default: off)
L: Lower bound for the interpolation parameter (not with -W/S) (default: 0)
U: Upper bound for the interpolation parameter (not with -W/S) (default: 1)
P: Determines the step size for tuning the interpolation parameter, nl. (U-L)/P (not with -W/S) (default: 10)
The Preliminary Attribute Check tests the underlying classifier against the DataTable specification at the inport of the node. Columns that are compatible with the classifier are marked with a green 'ok'. Columns which are potentially not compatible are assigned a red error message.
Important: If a column is marked as 'incompatible', it does not necessarily mean that the classifier cannot be executed! Sometimes, the error message 'Cannot handle String class' simply means that no nominal values are available (yet). This may change during execution of the predecessor nodes.
Capabilities: [Nominal attributes, Binary attributes, Unary attributes, Empty nominal attributes, Nominal class, Binary class, Missing class values] Dependencies:  min # Instance: 0
It shows the command line options according to the current classifier configuration and mainly serves to support the node's configuration via flow variables.
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To use this node in KNIME, install the extension KNIME Weka Data Mining Integration (3.7) from the below update site following our NodePit Product and Node Installation Guide:
A zipped version of the software site can be downloaded here.
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