MultiBoostAB

Package

weka.classifiers.meta

Synopsis

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).

Available in Weka 3.6.x - 3.7.1. Available via the package management system for Weka >= 3.7.2 (multiBoostAB).

Options

The table below describes the options available for MultiBoostAB.

Option	Description
classifier	The base classifier to be used.
debug	If set to true, classifier may output additional info to the console.
numIterations	The number of iterations to be performed.
numSubCmtys	Sets the (approximate) number of subcommittees.
seed	The random number seed to be used.
useResampling	Whether resampling is used instead of reweighting.
weightThreshold	Weight threshold for weight pruning.

Capabilities

The table below describes the capabilites of MultiBoostAB.

Capability	Supported
Class	Binary class, Nominal class, Missing class values
Attributes	Numeric attributes, Binary attributes, Nominal attributes, Unary attributes, Missing values, Empty nominal attributes, Date attributes
Min # of instances	1