SVM Backward¶

skfeature.function.wrapper.svm_backward

Description¶

SVM Backward is a wrapper method that starts from the full feature set and greedily removes the least useful feature at each step, based on the cross-validated accuracy of a Support Vector Machine.

Usage¶

import numpy as np
from sklearn.datasets import load_iris

from skfeature.function.wrapper import svm_backward

X, y = load_iris(return_X_y=True)

# rank features by cross-validated classifier performance
score = svm_backward.svm_backward(X, y)

Parameters¶

X: numpy array, shape (n_samples, n_features) — input data
y: numpy array, shape (n_samples,) — class labels
n_selected_features: int — number of features to select
mode: {"rank", "index"}, default "rank"

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.