Difference Between Feature Selection and Feature Extraction

Machine learning models require input features that are relevant and important to predict the outcome. However, not all features are equally important for a prediction task, and some features might even introduce noise in the model. Feature selection and feature extraction are two methods to handle this problem. In this article, we will explore the differences between feature selection and feature extraction methods in machine learning.

Feature Selection

Feature selection is a process of selecting a subset of relevant features from the original set of features. The goal is to reduce the dimensionality of the feature space, simplify the model, and improve its generalization performance. Feature selection methods can be categorized into three types:

• Filter Methods
• Wrapper methods
• Embedded methods.

Filter methods rank features based on their statistical properties and select the top-ranked features. Wrapper methods use the model performance as a criterion to evaluate the feature subset and search for the optimal feature subset. Embedded methods incorporate feature selection as a part of the model training process.

Here is an example of feature selection in the Recursive Feature Elimination (RFE) method. RFE is a wrapper method that selects the most important features by recursively removing the least important features and retraining the model. The feature ranking is based on the coefficients of the model.

Filter Methods

Filter methods are the simplest and most computationally efficient methods for feature selection. In this approach, features are selected based on their statistical properties, such as their correlation with the target variable or their variance. These methods are easy to implement and are suitable for datasets with a large number of features. However, they may not always produce the best results as they do not take into account the interactions between features.

Wrapper Methods

Wrapper methods are more sophisticated than filter methods and involve training a machine learning model to evaluate the performance of different subsets of features. In this approach, a search algorithm is used to select a subset of features that results in the best model performance. Wrapper methods are more accurate than filter methods as they take into account the interactions between features. However, they are computationally expensive, especially when dealing with large datasets or complex models.

Embedded Methods

Embedded methods are a hybrid of filter and wrapper methods. In this approach, feature selection is integrated into the model training process, and features are selected based on their importance in the model. Embedded methods are more efficient than wrapper methods as they do not require a separate feature selection step. They are also more accurate than filter methods as they take into account the interactions between features. However, they may not be suitable for all models as not all models have built-in feature selection capabilities.

Univariate Feature Selection

Univariate Feature Selection is a type of filter method used for feature selection. It involves selecting the features based on their individual performance in relation to the target variable. The most commonly used metric for this type of selection is the ANOVA F-value or chi-squared statistic for categorical data.

This is an example of the code implementation of Univariate Feature Selection using the ANOVA F-value metric in Python with scikit-learn:

Python3

from sklearn.feature_selection import SelectKBest,\
    f_classif
from sklearn.datasets import load_digits

# Load the digits dataset
X, y = load_digits(return_X_y=True)

# Perform univariate feature selection
# using ANOVA F-value metric
selector = SelectKBest(f_classif, k=10)
X_new = selector.fit_transform(X, y)

# Print the indices of the selected features
print(selector.get_support(indices=True))

Output:

[10 20 21 26 28 33 34 36 42 43]

In the above code, we use the SelectKBest class from scikit-learn’s feature_selection module to perform Univariate Feature Selection. We specify the ANOVA F-value metric using the f_classif function, which is suitable for classification tasks. We also set the value of k to 10, which means that we want to select the 10 best features.

The fit_transform method of the SelectKBest class is used to fit the selector to the data and transform the data by selecting the best features. Finally, we print the indices of the selected features using the get_support method of the selector object.

Generic Univariate Selections Methods

Generic Univariate Selection Methods refer to a group of Univariate Feature Selection methods that can be used with different metrics and scoring functions. Some commonly used Generic Univariate Selection Methods include SelectPercentile, SelectFpr, SelectFdr, and SelectFwe.

this is an example of the code implementation of Generic Univariate Selection using the SelectPercentile method in Python with scikit-learn:

Python3

from sklearn.feature_selection import SelectPercentile,\
    chi2
from sklearn.datasets import load_digits

# Load the digits dataset
X, y = load_digits(return_X_y=True)

# Perform univariate feature selection
# using SelectPercentile method
selector = SelectPercentile(score_func=chi2,
                            percentile=10)
X_new = selector.fit_transform(X, y)

# Print the indices of the selected features
print(selector.get_support(indices=True))

Output:

[30 33 34 42 43 54 62]

In the above code, we use the SelectPercentile class from scikit-learn’s feature_selection module to perform Generic Univariate Selection. We specify the chi-squared statistic as the scoring function using the chi2 function. We also set the value of percentile to 10, which means that we want to select the top 10% of features.

The fit_transform method of the SelectPercentile class is used to fit the selector to the data and transform the data by selecting the best features. Finally, we print the indices of the selected features using the get_support method of the selector object.

Feature Extraction

Feature extraction is a process of transforming the original features into a new set of features that are more informative and compact. The goal is to capture the essential information from the original features and represent it in a lower-dimensional feature space. Feature extraction methods can be categorized into linear methods and nonlinear methods.

• Linear methods use linear transformations such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) to extract features. PCA finds the principal components that explain the maximum variance in the data, while LDA finds the projection that maximizes the class separability.
• Nonlinear methods use nonlinear transformations such as Kernel PCA and Autoencoder to extract features. Kernel PCA uses kernel functions to map the data into a higher-dimensional space and finds the principal components in that space. Autoencoder is a neural network architecture that learns to compress the data into a lower-dimensional representation and reconstruct it back to the original space.
• Here is an example of feature extraction in the Mel-Frequency Cepstral Coefficients (MFCC) method. MFCC is a nonlinear method that extracts features from audio signals for speech recognition tasks. It first applies a filter bank to the audio signals to extract the spectral features, then applies the Discrete Cosine Transform (DCT) to the log-magnitude spectrum to extract the cepstral features.

Why feature selection/extraction is required?

Feature selection/extraction is an important step in many machine-learning tasks, including classification, regression, and clustering. It involves identifying and selecting the most relevant features (also known as predictors or input variables) from a dataset while discarding the irrelevant or redundant ones. This process is often used to improve the accuracy, efficiency, and interpretability of a machine-learning model.

Here are some of the main reasons why feature selection/extraction is required in machine learning:

1. Improved Model Performance: The inclusion of irrelevant or redundant features can negatively impact the performance of a machine learning model. Feature selection/extraction can help to identify the most important and informative features, which can lead to better model performance, higher accuracy, and lower error rates.
2. Reduced Overfitting: Including too many features in a model can cause overfitting, where the model becomes too complex and starts to fit the noise in the data instead of the underlying patterns. Feature selection/extraction can help to reduce overfitting by focusing on the most relevant features and avoiding the inclusion of noise.
3. Faster Model Training and Inference: Feature selection/extraction can help to reduce the dimensionality of a dataset, which can make model training and inference faster and more efficient. This is especially important in large-scale or real-time applications, where speed and performance are critical.
4. Improved Interpretability: Feature selection/extraction can help to simplify the model and make it more interpretable, by focusing on the most important features and discarding the less important ones. This can help to explain how the model works and why it makes certain predictions, which can be useful in many applications, such as healthcare, finance, and law.

Difference Feature Selection and Feature Extraction Methods

Feature selection and feature extraction methods have their advantages and disadvantages, depending on the nature of the data and the task at hand. 

	Feature Selection	Feature Extraction
1.	Selects a subset of relevant features from the original set of features.	Extracts a new set of features that are more informative and compact.
2.	Reduces the dimensionality of the feature space and simplifies the model.	Captures the essential information from the original features and represents it in a lower-dimensional feature space.
3.	Can be categorized into filter, wrapper, and embedded methods.	Can be categorized into linear and nonlinear methods.
4.	Requires domain knowledge and feature engineering.	Can be applied to raw data without feature engineering.
5.	Can improve the model’s interpretability and reduce overfitting.	Can improve the model performance and handle nonlinear relationships.
6.	May lose some information and introduce bias if the wrong features are selected.	May introduce some noise and redundancy if the extracted features are not informative.

At last, feature selection and feature extraction are two methods to handle the problem of irrelevant and redundant features in machine learning. Feature selection selects a subset of relevant features from