This repository contains the implementation of the various algorithms presented in Ensemble Methods in Ordinal Data Classification for Weka 3.7.
- oAdaBoost: oAdaBoost: An AdaBoost variant for Ordinal Data Classification
- AdaBoost.OR: Combining ordinal preferences by boosting
- AdaBoost.M1w: How to make AdaBoost.M1 work for weak base classifiers by changing only one line of the code
- oDT: Ensemble Methods in Ordinal Data Classification
- Ordinal Random Forests: Ensemble Methods in Ordinal Data Classification
- Download the pre-compiled package from https://github.com/JD557/weka-emiodc/releases
- Open Weka 3.7 and choose
Tools > Package Manager - Click on
File/URL(underUnofficial) and choose the downloaded .zip - Restart Weka
- You should now an
OrdinalEnsembleMethodspackage - The new classifiers should now be available
- Run
sbt package - Compress the compiled jar (
target/scala-2.10/emiodc_2.10-1.0.jar) alongside theDescription.propsin a zip - Open Weka 3.7 and choose
Tools > Package Manager - Click on
File/URL(underUnofficial) and choose the generated .zip - Restart Weka
- You should now an
OrdinalEnsembleMethodspackage - The new classifiers should now be available