genmrmr 0.1.0

Algorithm that uses genetic and MRMR for feature selection

GenMRMR

This repository proposes a new feature selection algorithm based on genetic and the MRMR algorithms. The algorithm aims to select a subset of features that maximizes classification accuracy. The algorithm works by creating a population of subsets of features, evaluating their weighted F1-score, and then hybridizing the best individuals to create new subsets. The probability of adding a feature during hybridization is estimated using the MRMR algorithm. Finally, the best subset of features is selected based on the F1-score.

Documentation

There are 3 methods available: fit, transform, fit_transform:

fit method is used to fit GenMRMR to given

Args:
    x_train, y_train, x_cv, y_cv(numpy.ndarray): train and cv data and labels that will be used for feature selection

transform method is used to transform fitted GenMRMR

Args:
    data(numpy.ndarray): data to transform

fit_transform method is used to fit and then transform data using GenMRMR

Args:
    data(pandas.core.frame.DataFrame): data to transform
    labels(pandas.core.series.Series): labels for data

Authors

• Algorithm created by Maksim Tislenko([e-mail])
  • Created the idea of algorithm
  • Implemented the whole algorithm
• Сomparison with existing feature selection algorithms was conducted with the participation of Rustam Paringer ([e-mail]) from the Department of Technical Cybernetics at Samara University.

Acknowledgements

I would also like to express my gratitude to Arkadiy Sotnikov ([e-mail]) for assistance in preparing this readme.