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 |