scikit-mlm 0.0.6

Minimal Learning Machine implementation using the scikit-learn API

scikit-mlm

scikit-mlm is a Python module implementing the Minimal Learning Machine (MLM) machine learning technique using the scikit-learn API.

quickstart

With NumPy, SciPy and scikit-learn available in your environment, install with:

pip3 install scikit-mlm

Classification example with the nearest neighbor MLM classifier:

from skmlm import NN_MLM
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import cross_val_score
from sklearn.pipeline import make_pipeline
from sklearn.datasets import load_iris

# load dataset
dataset = load_iris()

clf = make_pipeline(MinMaxScaler(), NN_MLM(rp_number=20))
scores = cross_val_score(clf, dataset.data, dataset.target, cv=10, scoring='accuracy')

print('AVG = %.3f, STD = %.3f' % (scores.mean(), scores.std()))

implemented methods

original proposal

• regression (MLMR)
• classification (MLMC)

speed up

• nearest neighbor MLM (NN_MLM)

reference points selection methods

classification

• opposite neighborhood MLM (ON_MLM)
• fuzzy C-means MLM (FCM-MLM)

cost Sensitive

• weighted MLM (w_MLM)

how to cite scikit-mlm

if you use scikit-mlm in your paper, please cite

@misc{scikit-mlm,
    author       = "Madson Luiz Dantas Dias",
    year         = "2019",
    title        = "scikit-mlm: A implementation of {MLM} for scikit framework",
    url          = "https://github.com/omadson/scikit-mlm",
    institution  = "Federal University of Cear\'{a}, Department of Computer Science" 
}

contributors

• Madson Dias

acknowledgement

• thanks for @JamesRitchie, the initial idea of this project is inspired on the scikit-rvm repo