Hakkwani / EnsembleMethodsForFeatureSelection Public

forked from agoscinski/EnsembleMethodsForFeatureSelection

Notifications You must be signed in to change notification settings
Fork 0
Star 1

By applying ensemble methods we improve the robustness of feature selection methods.

1 star 9 forks Branches Tags Activity

Notifications

Name		Name	Last commit message	Last commit date
Latest commit History 5 Commits
data-scripts		data-scripts
docs		docs
notebooks		notebooks
test		test
README.md		README.md
accuracy_measure.py		accuracy_measure.py
analyse_weights.py		analyse_weights.py
analysis.py		analysis.py
artificial_data.py		artificial_data.py
benchmarks.py		benchmarks.py
data_sets.py		data_sets.py
ensemble_methods.py		ensemble_methods.py
experiments.py		experiments.py
feature_selector.py		feature_selector.py
goodness_measure.py		goodness_measure.py
io_utils.py		io_utils.py
main.py		main.py
robustness_measure.py		robustness_measure.py
sklearn_utilities.py		sklearn_utilities.py

Repository files navigation

Ensemble methods for feature selection

Requirements

scikit-feature
Numpy
Matplotlib

Datasets

http://stats.stackexchange.com/questions/973/free-data-set-for-very-high-dimensional-classification

For testing:

MNIST: 64x1797 (Handwritten Digits)
Colon 2000x62 (Gene expression) http://genomics-pubs.princeton.edu/oncology/affydata/index.html
Gisette: 5000x13500 (Handwritten Digits) http://archive.ics.uci.edu/ml/datasets/Gisette
Arcena 10000x900 (mass-spectrometric) http://archive.ics.uci.edu/ml/datasets/Arcene

Final data sets:

Dexter: 20000x2600 (Bag of Word) http://archive.ics.uci.edu/ml/datasets/Dexter
Dorothea: 100000x1950 (Gene expression) http://archive.ics.uci.edu/ml/datasets/Dorothea
PEMS: 138672 x 440 http://archive.ics.uci.edu/ml/datasets/PEMS-SF

Results

About

By applying ensemble methods we improve the robustness of feature selection methods.

Report repository

Releases

No releases published

Packages

No packages published

Languages

Jupyter Notebook 88.0%
Python 11.9%
Shell 0.1%