ReliefF¶

skfeature.function.similarity_based.reliefF

Description¶

ReliefF is a supervised instance-based method. It estimates feature quality by repeatedly sampling an instance and updating feature weights based on its nearest neighbors from the same and different classes.

Usage¶

import numpy as np
from sklearn.datasets import load_iris

from skfeature.function.similarity_based import reliefF

X, y = load_iris(return_X_y=True)

# get a score for every feature (aligned with SelectKBest)
score = reliefF.reliefF(X, y)

# or get the indices of the selected features
selected = reliefF.reliefF(X, y, mode="index")

Parameters¶

X: numpy array, shape (n_samples, n_features) — input data
y: numpy array, shape (n_samples,) — class labels
**kwargs: optional k number of neighbors (default 5)
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¶

Original implementation from the DMML Lab@ASU Feature Selection Repository.