Bu-Refresher course PRESENTATION NEW.pptx

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) Learning Vector 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 Resonance Theory (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. Thank You