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.
- Type: Data Training data
- Type: Weka 3.7 Classifier Trained model
Analytics > Mining > Weka > Weka (3.7) > Classification Algorithms > misc
Make sure to have this extension installed:
KNIME Weka Data Mining Integration (3.7)
Update site for KNIME Analytics Platform 3.7:
KNIME Analytics Platform 3.7 Update Site