CHIRP (3.7)


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.

Input Ports

  1. Type: Data Training data

Output Ports

  1. Type: Weka 3.7 Classifier Trained model

Find here

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

How to install extensions