ML-MDLText

ML-MDLText is a multilabel text classifier based on minimum description length principle. ML-MDLText is implemented as described in our paper:

BITTENCOURT, MARCIELE M. ; SILVA, RENATO M. ; ALMEIDA, TIAGO A., ML-MDLText: An efficient and lightweight multilabel text classifier with incremental learning. APPLIED SOFT COMPUTING, v. 96, p. 1-15, 2020.

Additionally, a prototype version called ML-MDLText_α used in experiments of paper presented at BRACIS'19 is also available in this repository. ML-MDLText_αis implemented as described in our paper:

BITTENCOURT, MARCIELE M. ; SILVA, RENATO M. ; ALMEIDA, TIAGO A., ML-MDLText: A Multilabel Text Categorization Technique with Incremental Learning, 2019 8th Brazilian Conference on Intelligent Systems (BRACIS), Salvador, Brazil, 2019, pp. 580-585.

These implementations is written in Python and built to work with scikit-learn.

How to use

To get these implementations, execute the following command:

$ git clone https://github.com/m-bittencourt/ML-MDLText.git

You can add the following code to your Python source code to guide the interpreter to find the package successfully:

> import sys
> sys.path.append('src')

Import and initialize ML-MDLText or ML-MDLText_α classifier with a multiclass classification method as meta-model (clfClasses):

> from ML_MDLText_alpha import ML_MDLText_alpha
> classifier  = ML_MDLText_alpha(clfClasses)

> from ML_MDLText import ML_MDLText
> classifier  = ML_MDLText(clfClasses)

Then, you can train the classifier with the training data (x_train) and their respective labels ( y_train), according to the following code:

> classifier.fit(x_train, y_train)

And test with test data (x_test) and the following code:

> y_pred = classifier.predict(x_test)

Running an example

In this repository, there is an example of using of ML-MDLText and ML-MDLText_α with medical database and SGDClassifier as a meta-model, and can be executed through the following command:

$ python example.py

###Additional Information If you find ML-MDLText helpful, please cite it as:

@INPROCEEDINGS{Bittencourt:2019,
	author={Marciele M. Bittencourt and Renato Moraes Silva and Tiago A. Almeida},
	title={{ML-MDLText}: A Multilabel Text Categorization Technique with Incremental Learning},
	booktitle={2019 8th Brazilian Conference on Intelligent Systems (BRACIS19)}, 
	year={2019},
	month=oct,
	address = {Salvador, BA, Brasil},
	publisher={IEEE},
	doi={10.1109/BRACIS.2019.00107},
	ISSN={2643-6256},
	number={},
	pages={580-585}
}

If you find ML-MDLText helpful, please cite it as:

@article{BITTENCOURT2020106699,
	title = {ML-MDLText: An efficient and lightweight multilabel text classifier 	with incremental learning},
	author = {Marciele M. Bittencourt and Renato M. Silva and Tiago A. Almeida},
	journal = {Applied Soft Computing},
	volume = {96},
	pages = {106699},
	year = {2020},
	issn = {1568-4946},
	doi = {https://doi.org/10.1016/j.asoc.2020.106699}
}