# Feature selection using Particle Swarm Optimization¶

In this tutorial we’ll be using Particle Swarm Optimization to find an optimal subset of features for a SVM classifier. We will be testing our implementation on the UCI ML Breast Cancer Wisconsin (Diagnostic) dataset.

This tutorial is based on Jx-WFST, a wrapper feature selection toolbox, written in MATLAB by Jingwei Too.

## Dependencies¶

Before we get started, make sure you have the following packages installed:

• niapy: pip install niapy --pre

• scikit-learn: pip install scikit-learn

## Defining the problem¶

We want to select a subset of relevant features for use in model construction, in order to make prediction faster and more accurate. We will be using Particle Swarm Optimization to search for the optimal subset of features.

Our solution vector will represent a subset of features:

$x = [x_1, x_2, \dots , x_d]; x_i \in [0, 1]$

Where $$d$$ is the total number of features in the dataset. We will then use a threshold of 0.5 to determine whether the feature will be selected:

$\begin{split}\\& x_i= \begin{cases} 1, & \text{if}\ x_i > 0.5 \\ 0, & \text{otherwise} \end{cases}\end{split}$

The function we’ll be optimizing is the classification accuracy penalized by the number of features selected, that means we’ll be minimizing the following function:

$f(x) = \alpha \times (1 - P) + (1 - \alpha) \times \frac{N_selected}{N_features}$

Where $$\alpha$$ is the parameter that decides the tradeoff between classifier performance $$P$$ (classification accuracy in our case) and the number of selected features with respect to the number of all features.

## Implementation¶

First we’ll implement the Problem class, which implements the optimization function defined above. It takes the training dataset, and the $$\alpha$$ parameter, which is set to 0.99 by default.

For the objective function, the solution vector is first converted to binary, using the threshold value of 0.5. That gives us indices of the selected features. If no features were selected 1.0 is returned as the fitness. We then compute the mean accuracy of running 2-fold cross validation on the training set, and calculate the value of the optimization function defined above.

from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.svm import SVC

from niapy.problems import Problem
from niapy.algorithms.basic import ParticleSwarmOptimization

class SVMFeatureSelection(Problem):
def __init__(self, X_train, y_train, alpha=0.99):
super().__init__(dimension=X_train.shape, lower=0, upper=1)
self.X_train = X_train
self.y_train = y_train
self.alpha = alpha

def _evaluate(self, x):
selected = x > 0.5
num_selected = selected.sum()
if num_selected == 0:
return 1.0
accuracy = cross_val_score(SVC(), self.X_train[:, selected], self.y_train, cv=2, n_jobs=-1).mean()
score = 1 - accuracy
num_features = self.X_train.shape
return self.alpha * score + (1 - self.alpha) * (num_selected / num_features)


Then all we have left to do is load the dataset, run the algorithm and compare the results.

dataset = load_breast_cancer()
X = dataset.data
y = dataset.target
feature_names = dataset.feature_names

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state=1234)

problem = SVMFeatureSelection(X_train, y_train)
algorithm = ParticleSwarmOptimization(population_size=10, seed=1234)

selected_features = best_features > 0.5
print('Number of selected features:', selected_features.sum())
print('Selected features:', ', '.join(feature_names[selected_features].tolist()))

model_selected = SVC()
model_all = SVC()

model_selected.fit(X_train[:, selected_features], y_train)
print('Subset accuracy:', model_selected.score(X_test[:, selected_features], y_test))

model_all.fit(X_train, y_train)
print('All Features Accuracy:', model_all.score(X_test, y_test))


Output:

Number of selected features: 4
Selected features: mean smoothness, mean concavity, mean symmetry, worst area
Subset accuracy: 0.9210526315789473
All Features Accuracy: 0.9122807017543859