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Questions and Answers
What is Feature Subset Selection (FSS)?
Select a subset of features from a given feature set.
What is the goal of Feature Subset Selection?
Optimize an objective function for the chosen features.
Which of the following are methods used in Feature Subset Selection? (Select all that apply)
What is one reason for doing Feature Subset Selection instead of feature extraction?
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What does low variance in features indicate?
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What is the relationship between correlated features?
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What does mutual information measure?
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Which of the following is a statistical measure used in Filter methods for FSS? (Select all that apply)
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Features with high mutual information are more likely to be predictive of each other compared to features with _____ mutual information.
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Study Notes
What is Feature Subset Selection (FSS)?
- Feature subset selection is the process of choosing a subset of features from a given set.
- The goal of FSS is to optimize an objective function for the chosen features.
- FSS is different from feature extraction, which transforms existing features into a lower-dimensional space.
- FSS selects a subset of existing features without transformation.
Why do Feature Subset Selection?
- Feature values may be expensive to collect.
- FSS can help extract meaningful rules from data mining models.
- Features may not be numeric, which is a common situation in data mining.
- Fewer features can lead to a simpler model, which can improve generalization capabilities and avoid overfitting.
- Reduced model complexity can also reduce running time.
Why is Feature Selection Challenging?
- The number of possible feature subsets grows exponentially with the number of features.
- For example, with 3 features, there are 7 possible subsets. With 100 features, the number of subsets is astronomically large.
Taxonomy of Feature Selection Methods
- Feature selection methods can be broadly categorized as unsupervised or supervised.
Unsupervised Feature Selection
- Unsupervised methods evaluate features independently of the target variable.
- Common measures used include variance, correlation, and mutual information.
Variance Filter
- Variance filters remove features with a high percentage of identical values across all objects.
- Low variance indicates that a feature may not be informative.
Correlation Filter
- Correlation filters remove highly correlated input features.
- Correlated features indicate redundant information.
- Correlation can only detect linear relationships between features.
Mutual Information Filter
- Mutual information measures the amount of information that two categorical features share.
- Features with high mutual information are more likely to be predictive of each other.
- Mutual information can detect any kind of relationship between two features.
Filter Methods for FSS
- Filter methods evaluate features independently of the target variable.
- Common statistical measures used include correlation with the target variable, information gain, and chi-square test.
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Description
This quiz explores the concepts and challenges of Feature Subset Selection (FSS) in data mining. It covers the importance of FSS, its objectives, and the differences between FSS and feature extraction. Additionally, the quiz addresses the complexities involved in selecting features effectively.
