Uploaded by Meroo Bakkar

Feature selection

Feature selection
Feature Selection refers to the selection of the most appropriate subset of
features that describes (adequately) a given classification task.
Key feature selection methods:
- Open-loop (filter/ front-end/ preset bias)
- Closed-loop (wrapper/ performance bias)
1- Open-loop methods (FILTER, preset bias, front end):
Select features for which the reduced data set maximizes between-class
separability (by evaluating within-class and between-class covariance
matrices ); no feedback mechanism from the processing algorithm.
2-Closed-loop methods (WRAPPER, performance bias, classifier feedback):
Select features based on the processing algorithm performance (feedback
mechanism), which serves as a criterion for feature subset selection
Feature selection has four different approaches
 Filter approach
 Wrapper approach
 Embedded approach
Hybrid approach
Filter approach
A subset of features is selected by this approach without using any learning algorithm.
Higher-dimensional datasets use this method and it is relatively faster than the wrapperbased approaches
Independent of classification model
Uses only dataset of annotated examples
A relevance measure for each feature is calculated:
E.g: Feature – Class entropy
Kullback-Leibler divergence (cross-entropy)
Information gain, gain ratio
Normalize relevance scores weights
Fast, but discards feature dependencies
Wrapper approach
This approach has high computational complexity. It uses a learning algorithm to
evaluate the accuracy produced by the use of the selected features in classification.
Wrapper methods can give high classification accuracy for particular classifiers
 Specific to a classification algorithm
 The search for a good feature subset is guided by a
search algorithm (e.g. greedy forward or backward)
 The algorithm uses the evaluation of the classifier
as a guide to find good feature subsets
 Examples: sequential forward or backward search,
simulated annealing, stochastic iterative sampling
(e.g. GA, EDA)
 Computationally intensive, but able to take into
account feature dependencies
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