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What is the feature subset selection process in machine learning?
Introduction
The success of machine learning algorithms depends on the quality of the data they use to extract knowledge. Machine learning algorithms may produce inaccurate or unintelligible results if data is inadequate or contains irrelevant information. By removing irrelevant and redundant information before learning, feature subset selection algorithms aim to reduce the amount of time it takes to learn. It reduces data dimensionality, improves algorithm efficiency, and enhances performance and interpretability.
Feature subsets are evaluated using a correlation-based heuristic in a new feature selection algorithm. Three common machine learning algorithms are used to evaluate the algorithm's effectiveness, and experiments on standard datasets show that the algorithm improves significantly. The article discusses how feature selection enhances machine learning performance and its application in different areas.
Feature Subset Selection: Filter vs. Wrapper Methods
Wrapper Method
Borrows techniques from statistics and pattern recognition.
Utilizes statistical resampling (e.g., cross-validation) with the actual machine learning algorithm to estimate accuracy of feature subsets.
Slow execution due to repeated calls to the induction algorithm.
Useful but not efficient for large datasets with many features.
Filter Method
Operates independently of any specific induction algorithm.
Filters out undesirable features before the induction process.
Utilizes all training data for feature selection.
Faster compared to wrapper methods.
Suitable for large datasets with a high number of features.
Types of Filter Methods
Consistency−based filters:
Seek feature subsets where every combination of values corresponds to a single class label.
Redundancy−based filters:
Eliminate features with redundant information that can be inferred from other remaining features.
Relevancy−based filters:
Rank features based on their relevancy score.
Advantages of Filter Methods
Faster execution compared to wrapper methods.
Well−suited for large datasets with many features.
Reduces dimensionality and improves efficiency of subsequent machine learning algorithms.
Operates independently of the induction algorithm.
In summary, while the wrapper method estimates accuracy by invoking the induction algorithm repeatedly, the filter method operates independently and filters out undesirable features before the induction process. Filter methods are faster, making them suitable for large datasets, and they help reduce dimensionality and improve the efficiency of subsequent machine learning algorithms.
Code snippet
Here's an example code snippet that demonstrates a basic implementation of a filter−based feature subset selection:
```python
import numpy as np
from sklearn.feature_selection import SelectKBest, mutual_info_classif
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score
# Generate dummy dataset
X = np.random.rand(1000, 20) # 1000 samples, 20 features
y = np.random.randint(0, 2, 1000) # Binary class labels
# Split the dataset into train and test sets
Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, test_size=0.2, random_state=25)
# Feature selection using filter method
num_features = 10
select = SelectKBest(score_func=mutual_info_classif, k=num_features)
Xtraiselected = select.fit_transform(X_train, y_train)
Xtesselected = select.transform(X_test)
# Train a classifier on the selected features
classy = KNeighborsClassifier(n_neighbors=3)
classy.fit(X_traiselected, y_train)
# Evaluate the classifier on the test set
y_pred = classifier.predict(Xtesselected)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
```
Create a dummy dataset with 1000 samples and 20 characteristics for this example. Then, using scikit-learn's 'train_test_split' function, divide the data into training and test sets. Then, as the scoring function, use a filter-based feature selection method with the 'SelectKBest' class and the mutual information criterion ('mutual_info_classif'). You can define the number of features to select ('num_features').
Then, using the feature selector's 'fit_transform' and 'transform' methods, transform the training and test sets using the features. Then, initialise a K-nearest neighbours classifier ('KNeighborsClassifier') and train it on the training set's specified features.
Finally, the trained classifier generates predictions on the test set and evaluates their correctness using the 'accuracy_score' function. The resulting precision is satisfactory.
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