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MultiBoostAB (3.7)

KNIME WEKA nodes (3.7) version 4.4.0.v202104271009 by KNIME AG, Zurich, Switzerland

Class for boosting a classifier using the MultiBoosting method. MultiBoosting is an extension to the highly successful AdaBoost technique for forming decision committees

MultiBoosting can be viewed as combining AdaBoost with wagging.It is able to harness both AdaBoost's high bias and variance reduction with wagging's superior variance reduction.

Using C4.5 as the base learning algorithm, Multi-boosting is demonstrated to produce decision committees with lower error than either AdaBoost or wagging significantly more often than the reverse over a large representative cross-section of UCI data sets.It offers the further advantage over AdaBoost of suiting parallel execution.

For more information, see

Geoffrey I.

Webb (2000).MultiBoosting: A Technique for Combining Boosting and Wagging.

Machine Learning.Vol.40(No.2).

(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.


MultiBoostAB Options

C: Number of sub-committees. (Default 3)

P: Percentage of weight mass to base training on. (default 100, reduce to around 90 speed up)

Q: Use resampling for boosting.

S: Random number seed. (default 1)

I: Number of iterations. (default 10)

D: If set, classifier is run in debug mode and may output additional info to the console

W: Full name of base classifier. (default: weka.classifiers.trees.DecisionStump)

D: If set, classifier is run in debug mode and may output additional info to the console

Select target column
Choose the column that contains the target variable.
Preliminary Attribute Check

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, Date attributes, Missing values, Nominal class, Binary class, Missing class values] Dependencies: [Nominal attributes, Binary attributes, Unary attributes, Empty nominal attributes, Numeric attributes, Date attributes, String attributes, Relational attributes, Missing values, No class, Missing class values, Only multi-Instance data] min # Instance: 1

Command line options

It shows the command line options according to the current classifier configuration and mainly serves to support the node's configuration via flow variables.

Additional Options

Select optional vector column
If the input table contains vector columns (e.g. double vector), the one to use can be selected here. This vector column will be used as attributes only and all other columns, except the target column, will be ignored.
Keep training instances
If checked, all training instances will be kept and stored with the classifier model. It is useful to calculate additional evaluation measures (see Weka Predictor) that make use of class prior probabilities. If no evaluation is performed or those measures are not required, it is advisable to NOT keep the training instances.

Input Ports

Training data

Output Ports

Trained model


Weka Node View
Each Weka node provides a summary view that provides information about the classification. If the test data contains a class column, an evaluation is generated.


To use this node in KNIME, install KNIME Weka Data Mining Integration (3.7) from the following update site:


A zipped version of the software site can be downloaded here.

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