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README.md
requirements.txt
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README.md

Co-training algorithm on Educational Data Mining

An implementation of the Co-training scheme, a well-known multi-view Semi-Supervised Learning approach, applied on Educational Data Mining datasets related with the task of Early Prognosis of Academic Performance is provided here.

Apart from the proposed algorithm Cotrain(Extra, GBC), the results of several other variant of Co-training scheme are provided, as well as the results of Self-training approaches. Moreover, the results of the CoForest algorithm (implemented by Mr. Ming Li lim@lamda.nju.edu.cn - link) have been computed for the same datasets' splits and same seeds.

Datasets

A full description is provided on the related publication. More comments are going to be posted, after the acceptance of the submission.

Citation

Please cite this paper if you use our algorithm/datasets for your work.

'''

Submitted on http://ieee-edusociety.org/about/about-ieee-transactions-learning-technologies

Early access: https://ieeexplore.ieee.org/document/8692618

'''

Basic Dependencies

  • Python 2.7

  • Python dependencies for main algorithm

pip install -r requirements.txt

  • Python 3.x

  • Python dependencies for visualizations algorithm

pip install -r requirements_draw.txt

Notes

More information about the authors are provided in ml.math.upatras.gr

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