{
    "info": {
        "author": "Madson Dias",
        "author_email": "madsonddias@gmail.com",
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        "description": "# scikit-mlm\n\n![GitHub](https://img.shields.io/github/license/omadson/scikit-mlm.svg)\n[![PyPI](https://img.shields.io/pypi/v/scikit-mlm.svg)](http://pypi.org/project/scikit-mlm/)\n[![GitHub commit activity](https://img.shields.io/github/commit-activity/w/omadson/scikit-mlm.svg)](https://github.com/omadson/scikit-mlm/pulse)\n[![GitHub last commit](https://img.shields.io/github/last-commit/omadson/scikit-mlm.svg)](https://github.com/omadson/scikit-mlm/commit/master)\n[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.2875802.svg)](https://doi.org/10.5281/zenodo.2875802)\n\n\n\n`scikit-mlm` is a Python module implementing the [Minimal Learning Machine][1] (MLM) machine learning technique using the [scikit-learn][2] API.\n\n## instalation\nthe `scikit-mlm` package is available in [PyPI](https://pypi.org/project/scikit-mlm/). to install, simply type the following command:\n```\npip install scikit-mlm\n```\n\n## basic usage\nexample of classification with the [nearest neighbor MLM](https://link.springer.com/article/10.1007%2Fs11063-017-9587-5#Sec9) classifier:\n```Python\nfrom skmlm import NN_MLM\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.datasets import load_iris\n\n# load dataset\ndataset = load_iris()\n\nclf = make_pipeline(MinMaxScaler(), NN_MLM(rp_number=20))\nscores = cross_val_score(clf, dataset.data, dataset.target, cv=10, scoring='accuracy')\n\nprint('AVG = %.3f, STD = %.3f' % (scores.mean(), scores.std()))\n```\n\n## how to cite scikit-mlm\nif you use `scikit-mlm` in your paper, please cite it in your publication.\n```\n@misc{scikit-mlm,\n    author       = \"Madson Luiz Dantas Dias\",\n    year         = \"2019\",\n    title        = \"scikit-mlm: An implementation of {MLM} for scikit-learn framework\",\n    url          = \"https://github.com/omadson/scikit-mlm\",\n    doi          = \"10.5281/zenodo.2875802\",\n    institution  = \"Federal University of Cear\\'{a}, Department of Computer Science\" \n}\n```\n\n## contributing\n\nthis project is open for contributions. here are some of the ways for you to contribute:\n - bug reports/fix\n - features requests\n - use-case demonstrations\n\nto make a contribution, just fork this repository, push the changes in your fork, open up an issue, and make a pull request!\n\n## list of implemented technics\n - [original regression (MLMR)](https://doi.org/10.1016/j.neucom.2014.11.073)\n - [original classification (MLMC)](https://doi.org/10.1016/j.neucom.2014.11.073)\n - [nearest neighbor MLM (NN_MLM)](https://link.springer.com/article/10.1007%2Fs11063-017-9587-5#Sec9)\n - [opposite neighborhood MLM (ON_MLM)](https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2018-198.pdf)\n - [fuzzy C-means MLM (FCM_MLM)](https://doi.org/10.1007/978-3-319-95312-0_34)\n - [optimally selected MLM (OS_MLM)](https://doi.org/10.1007/978-3-030-03493-1_70)\n - [&ell;<sub>1/2</sub>-norm regularization MLM (L12_MLM)](https://doi.org/10.1109/BRACIS.2018.00043)\n - [weighted MLM (w_MLM)](https://doi.org/10.1007/978-3-319-26532-2_61)\n - [ranking MLM (R-MLM)](https://doi.org/10.1109/BRACIS.2015.39) (WIP)\n\n\n\n## future improvements\n\nlist of methods that will be implemented in the next releases:\n - [cubic equation MLM (C-MLM)](https://link.springer.com/article/10.1007%2Fs11063-017-9587-5#Sec10)\n - [expected squared distance MLM (ESD-MLM)](https://doi.org/10.1007/978-3-319-26532-2_62)\n - [voting based MLM (V-MLM)](https://link.springer.com/article/10.1007%2Fs11063-017-9587-5#Sec11)\n - [weighted voting based MLM (WV-MLM)](https://link.springer.com/article/10.1007%2Fs11063-017-9587-5#Sec11)\n - [random sampling voting based MLM (RSV-MLM)](https://link.springer.com/article/10.1007%2Fs11063-017-9587-5#Sec11)\n - [random sampling weighted voting based MLM (RSWV-MLM)](https://link.springer.com/article/10.1007%2Fs11063-017-9587-5#Sec11)\n - [reject option MLM (renjo-MLM)](https://doi.org/10.1109/BRACIS.2016.078)\n - [reject option weighted MLM (renjo-wMLM)](https://doi.org/10.1109/BRACIS.2016.078)\n\n\n<!-- #### regression\n - [ ] [regularized M-FOCUSS MLM (RMF_MLM)]() -->\n\n<!-- ### speed up\n### missing values\n### ensemble \n### reject option\n### ranking -->\n\n## contributors\n - [Madson Dias](https://github.com/omadson)\n\n## acknowledgement\n - thanks for [@JamesRitchie](https://github.com/JamesRitchie), the initial idea of this project is inspired on the [scikit-rvm](https://github.com/JamesRitchie/scikit-rvm) repo\n\n\n[1]: https://doi.org/10.1016/j.neucom.2014.11.073\n[2]: http://scikit-learn.org/\n[3]: https://doi.org/10.1007/s11063-017-9587-5#\n\n\n",
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