ASDD

1. Feature Selection in Machine
Learning
Presented by
R.Sridevi
Assistant Professor,
Department of Computer Science,
Tagore Govt.Arts & Science College,
Puducherry.

2. Agenda
Feature Selection
Study on Feature Selection Methods
Feature Selection in Machine Learning
Application of Feature Selection
Conclusion

3. Feature Selection
 Technique for identifying the most important features for learning.
 Increases learning performance by reducing the dimensionality of
data.
Subset
Validation

4. Filter Approach
 Filtering methods are independent of the induction algorithm.
 Evaluate each feature individually based on its correlation with the target function.
 Filter out irrelevant attributes before induction occurs
Input Features Feature Subset Selection Induction Algorithm

5. Wrapper Approach
Input Features Induction Algorithm
Wrapper Approach
Feature Subset Evaluation
Feature Subset Search
Induction Algorithm
A generic approach for feature selection occurs outside the basic
induction method but uses that method as a subroutine.
Searches the same space of feature subsets as filter methods, but it
evaluates alternative sets by running some induction algorithm on the
training data.

6. Relevant, Irrelevant & Redundant Features
 purpose of a Feature Selection is to identify relevant features according to
a definition of relevance.
 Relevance with respect to Target
 Strong Relevance to sample
 Weak Relevance to the sample
 presence of irrelevant attributes should considerably slow the rate of
learning.
 Redundant features exists whenever a feature can take the role of another.

7. Existing Feature Selection Methods
Feature weighting/ ranking algorithms.
Floating Search Methods in Feature Selection.
Mutual Information Based Algorithms.
Correlation based Algorithms.
Feature Interaction based Algorithms.

8. Categorization of Feature Selection
Methods
FSA Supporting Aspects
Relief, Relief-F,ABB, LVF Noise Tolerant
FOCUS, FCBF, Consistency,
mRMR
Eliminate Redundant Features
Filter Model based on GA Eliminate Irrelevant Features
MIFS, FSBAR, ABB,
CBFS,FRFS,IWFS
Eliminate Redundant & Irrelevant
Features
SAGA, INTERACT,
FSBAR,FRFS,IWFS Feature Interaction

9. Neural Network based Machine Learning
Supervised-Learning NN
Feed-Forward NN
 Perceptron
 Backpropagation (BP)
 LearningVector Quantization (LVQ)
Recurrent NN
 Fuzzy Cognitive Map (FCM)
 Boltzmann Machine (BM)
Unsupervised-Learning NN
Feed-Forward NN
 Learning Matrix(LM)
 Fuzzy Associative Memory (FAM)
Recurrent NN
 Binary Adaptive ResonanceTheory (ART1)
 Kohonen Self- Organizing Map (SOM)

10. Applications of Feature Selection
Text Mining
Image Processing
Bio- Informatics
Industrial Applications

11. Conclusion
Feature Selection improves accuracy during classification.
Enables the machine learning algorithm to train faster.
Avoid the curse of high dimensionality.

12. ThankYou