Fuzzy_Systems_Classification

TSK models that are using the hybrid method for training. For the task 1 use Haberman's dataset. For the task 2 use Epileptic Seizure Recognition Data Set

Task 1 Model 1

• Use Subtractive Clustering
• Class Independent for clusterInfluenceRange = 0.7
• Change the output function to constant
• Train the TSK model with hybrid method (Backpropagation and Least Squares Method)
• Evaluate the model
  • Error matrix
  • Producer’s accuracy – User’s accuracy
  • Overall accuracy
  • K

Task 1 Model 2

• Use Subtractive Clustering
• Class Independent for clusterInfluenceRange = 0.9
• Change the output function to constant
• Train the TSK model with hybrid method (Backpropagation and Least Squares Method)
• Evaluate the model
  • Error matrix
  • Producer’s accuracy – User’s accuracy
  • Overall accuracy
  • K

Task 1 Model 3

• Use Subtractive Clustering
• Class Dependent for clusterInfluenceRange = 0.7
• Change the output function to constant
• Train the TSK model with hybrid method (Backpropagation and Least Squares Method)
• Evaluate the model
  • Error matrix
  • Producer’s accuracy – User’s accuracy
  • Overall accuracy
  • K

Task 1 Model 4

• Use Subtractive Clustering
• Class Dependent for clusterInfluenceRange = 0.9
• Change the output function to constant
• Train the TSK model with hybrid method (Backpropagation and Least Squares Method)
• Evaluate the model
  • Error matrix
  • Producer’s accuracy – User’s accuracy
  • Overall accuracy
  • K

Task 2

• Use Subtractive Clustering
• Create a grid search for the best number of features and values of radii
• Use relieff for feature selection
• Compare metrics between models to find the best parameters