Decorate (3.7)

Learner

DECORATE is a meta-learner for building diverse ensembles of classifiers by using specially constructed artificial training examples

Comprehensive experiments have demonstrated that this technique is consistently more accurate than the base classifier, Bagging and Random Forests.Decorate also obtains higher accuracy than Boosting on small training sets, and achieves comparable performance on larger training sets.

For more details see:

P.

Melville, R.J.

Mooney: Constructing Diverse Classifier Ensembles Using Artificial Training Examples.In: Eighteenth International Joint Conference on Artificial Intelligence, 505-510, 2003.

P.

Melville, R.J.

Mooney (2004).Creating Diversity in Ensembles Using Artificial Data.

Information Fusion: Special Issue on Diversity in Multiclassifier Systems..

(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

Extension

This node is part of the extension

KNIME Weka Data Mining Integration (3.7)

v4.0.0

Short Link

Drag node into KNIME Analytics Platform