pdeseg 0.1.3

A feature selection method based on identifying features that best segregate classes via their underlying probablity density estimations

PDE-Seg (PDE-Segregate) is a univariate filter feature selection method based on a filter-measure that ranks features according to their ability to segregate the probability density estimates (PDE) of the class samples.

Install

PDE-Seg can be installed from PyPI:

pip install pdeseg

Example

from pdeseg import PDE_Segregate

import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_classification

# Create random classification dataset
X, y = make_classification(
    n_samples=300, n_features=50, n_classes=3, n_informative=5,
    shuffle=False
)

# Initialize PDE-Segregate object
pdeRanker = PDE_Segregate()

# Carry out feature selection
pdeRanker.fit(X, y)

# Get top 10 features
top10Features = pdeRanker.get_topnFeatures(10)

# Visualize the top feature's ability to segregate PDEs
fig, axs = plt.subplots(1, 2, sharey=True)

# Top ranked feature
pdeRanker.plot_overlapAreas(top10Features[0], legend="intersection", _ax=axs[0])
axs[0].set_title("Most relevant feature", loc="left")

# Last ranked feature
pdeRanker.plot_overlapAreas(49, legend="intersection", _ax=axs[1])
axs[1].set_title("Least relevant feature", loc="left")

axs[0].set_ylabel(r"Probability Density, $\hat{P}$")
for i in range(2):
    axs[i].set_xlim(-0.5, 1.5)
    axs[i].set_xticks(np.arange(-0.5, 2.0, 0.5))

Check out the notebooks provided as tutorials and examples of some specific use cases.

Citation

For now, cite the followinng abstract

J.C. Liaw, F. Geu Flores. A novel univariate feature selection filter-measure based on the reduction of class overlapping. 94th Annual Meeting of the International Association of Applied Mathematics and Mechanics - GAMM, Magdeburg, Deutschland, 18.-22. March 2024, Oral Presentation S25.01-4

Available at Book of Abstracts of the 94th Annual Meeting of the International Association of Applied Mathematics and Mechanics, p363

The other feature selection methods that were compared to in our paper is as listed below:

1. LH-RELIEF: Feature weight estimation for gene selection: a local hyperlinear learning approach DOI: https://doi.org/10.1186/1471-2105-15-70

2. I-RELIEF: Iterative RELIEF for Feature Weighting: Algorithms, Theories, and Applications DOI: https://doi.org/10.1109/TPAMI.2007.1093

3. RELIEF-F: Estimating attributes: Analysis and extensions of RELIEF DOI: https://doi.org/10.1007/3-540-57868-4_57

4. MultiSURF: Benchmarking relief-based feature selection methods for bioinformatics data mining DOI: https://doi.org/10.1016/j.jbi.2018.07.015

5. Random Forests DOI: https://doi.org/10.1023/A:1010933404324

6. ANOVA F-statistic: Statistical Methods for Research Workers

7. Mutual Information: Estimating mutual information DOI: https://doi.org/10.1103/PhysRevE.69.066138