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Deep ensemble-elastic self-organized map (deesom): a SOM based classifier to deal with large and highly imbalanced data.

Project description


Self-organized map based classifier, developed to deal with large and highly imbalanced data.

The methods automatically build several layers of SOM. Data is clustered and samples that are not likely to be positive class member are discarded at each level.

The elastic-deepSOM (elasticSOM) is a deep architecture of SOM layers where the map size is automatically set in each layer according to the data filtered in each previous map. The ensemble-elasticSOM (eeSOM) uses several SOMs in ensemble layers to face the high imbalance challenges. These new models are particularly suited to handle problems where there is a labeled class of interest (positive class) that is significantly under-represented with respect to a higher number of unlabeled data.

This code can be used, modified or distributed for academic purposes under GNU GPL. Please feel free to contact with any issue, comment or suggestion.

This code was used in:

"Deep neural architectures for highly imbalanced data in bioinformatics" L. A. Bugnon, C. Yones, D. H. Milone and G. Stegmayer, IEEE Transactions on Neural Networks and Learning Systems, Special Issue on Recent Advances in Theory, Methodology and Applications of Imbalanced Learning (in press).

sinc(i) -


just do:

python -m pip install --user -U deeSOM

Running the demo

You'll find a Jupyter notebook with a small tutorial to train a deeSOM model and use it for predictions.

Project details

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Files for deeSOM, version 1.0
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Filename, size deeSOM-1.0-py3-none-any.whl (25.4 kB) File type Wheel Python version py3 Upload date Hashes View
Filename, size deeSOM-1.0.tar.gz (19.8 kB) File type Source Python version None Upload date Hashes View

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