GOOWE-ML

GOOWE-ML is a stacked online ensemble for MLSC (multi-label stream classification) task. Its name is an abbreviation for Geometrically-Optimum Online Weighted Ensemble for Multi-Label Classification.

It is introduced in the ACM CIKM 2018 paper:

"A Novel Online Stacked Ensemble for Multi-Label Stream Classification" by Alican Büyükçakır, Hamed Bonab and Fazli Can.

Dependencies

• MOA
• MEKA
• Jama - For matrix operations such as solving LSQ.
• sizeofag - for measuring memory consumption of each model

Datasets

Can be downloaded from http://meka.sourceforge.net/#datasets. In case of this link crashing, I put the datasets that are used in our experiments into this repository as well.

Running Models

Assuming you generated .jar files that run the main method for the files RunClassifiers.java and RunGOOWEs.java

Create the following directories in the same directory as your jar files:

• ./output/final-results
• ./output/statistics

The jar file will generate window-based evaluations of the models (in detailed and short formats) in the former; overall results wrt many metrics in the latter.

Run the experiments in the following format (arrange the virtual memory size according to your config.):

java -jar -Xmx32G -javaagent:sizeofag.jar Model.jar Dataset.arff NumLabels BatchSize AlgorithmNo

For instance, WLOG, for the dataset 20NG, run the experiments as:

• For GOOWE-ML-based ensembles:

java -jar -Xmx32G -javaagent:sizeofag.jar RunGOOWEs.jar 20NG-F.arff 20 1000 ${j}

where ${j} = 1 to 4 corresponds to [GOBR, GOCC, GOPS, GORT].

• For the baselines:

java -jar -Xmx32G -javaagent:sizeofag.jar RunClassifiers.jar 20NG-F.arff 20 1000 ${j}

where ${j} = 1 to 7 corresponds to [EBR, ECC, EPS, EBRT, EaBR, EaCC, EaPS].

To cite, use the following bibtex entry:

@inproceedings{buyukccakir2018novel,
  title={A novel online stacked ensemble for multi-label stream classification},
  author={B{\"u}y{\"u}k{\c{c}}ak{\i}r, Alican and Bonab, Hamed and Can, Fazli},
  booktitle={Proceedings of the 27th ACM International Conference on Information and Knowledge Management},
  pages={1063--1072},
  year={2018},
  organization={ACM}
}