Hello Yuvaraj,
To calculate classification accuracy using a Fuzzy Inference System (FIS) generated from subtractive clustering, you should follow these steps:
- Evaluate the FIS on Test Data: You've already done this with evalfis, which generates fuzzy output predictions for your test data.
- Defuzzify the Output: If your FIS output is continuous, you might need to map it to discrete class labels. This often involves setting thresholds or using a method to determine which class a continuous output belongs to.
- Calculate Classification Accuracy: Compare the predicted class labels against the true labels to compute the accuracy.
Here is how you can implement these steps in MATLAB:
% Assuming 'testfuzout' is the fuzzy output for the test data
% and 'foutputtest' contains the true labels
% Step 2: Defuzzify the output
% For simplicity, let's assume you have two classes [0, 1]
% You might need to adjust this logic based on your specific classes
threshold = 0.5; % Example threshold for binary classification
predictedLabels = testfuzout >= threshold;
% Step 3: Calculate classification accuracy
correctPredictions = (predictedLabels == foutputtest);
accuracy = sum(correctPredictions) / length(foutputtest) * 100;
fprintf('Classification Accuracy: %.2f%%\n', accuracy);
I hope this helps!
