MIBoost
Package
weka.classifiers.mi
Synopsis
MI AdaBoost method, considers the geometric mean of posterior of instances inside a bag (arithmatic mean of log-posterior) and the expectation for a bag is taken inside the loss function.
For more information about Adaboost, see:
Yoav Freund, Robert E. Schapire: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, 148-156, 1996.
Options
The table below describes the options available for MIBoost.
Option |
Description |
|---|---|
classifier |
The base classifier to be used. |
debug |
If set to true, classifier may output additional info to the console. |
discretizeBin |
The number of bins in discretization. |
maxIterations |
The maximum number of boost iterations. |
Capabilities
The table below describes the capabilites of MIBoost.
Capability |
Supported |
|---|---|
Class |
Binary class, Missing class values |
Attributes |
String attributes, Date attributes, Nominal attributes, Empty nominal attributes, Binary attributes, Missing values, Numeric attributes, Unary attributes, Relational attributes |
Other |
Only multi-Instance data |
Min # of instances |
0 |