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CFS

skfeature.function.statistical_based.CFS

Description

CFS (Correlation-based Feature Selection) evaluates feature subsets by a merit function that rewards features highly correlated with the class labels but uncorrelated with each other, using symmetrical uncertainty as the correlation measure.

Usage

import numpy as np
from sklearn.datasets import load_iris
from sklearn.feature_selection import SelectKBest

from skfeature.function.statistical_based import CFS

X, y = load_iris(return_X_y=True)

# rank features and select the top k
selector = SelectKBest(score_func=CFS.cfs, k=2)
X_selected = selector.fit_transform(X, y)

Parameters

  • X: numpy array, shape (n_samples, n_features) — input data
  • y: numpy array, shape (n_samples,) — class labels
  • mode: {{"rank", "index"}}, default "rank" — "rank" returns an array of feature indices ordered by importance and aligned with sklearn.feature_selection.SelectKBest; "index" returns the indices of the selected features with the most important one first

Returns

  • score: numpy array, shape (n_features,) — ranking score of every feature, aligned with sklearn.feature_selection.SelectKBest

References

  • Hall, Mark A. "Correlation-based feature selection for machine learning." PhD thesis 1999.