CHIRP is an iterative sequence of three stages (projecting, binning, and covering) that are designed to deal with the curse of dimensionality, computational complexity, and nonlinear separability
CHIRP classifies with a set cover on iterated random projections.For more information, see: CHIRP: A New Classifier Based on Composite Hypercubes on Iterated Random Projections.
Proceedings of the ACM KDD 2011.
(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.
V: Specifies the number of voter
S: Random number seed. (default 1)
D: If set, classifier is run in debug mode and may output additional info to the console
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, Numeric attributes, String attributes, Missing values, Nominal class, Binary class, Missing class values] Dependencies:  min # Instance: 1
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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