DMData Mining Techniques
All experiments

Experiment 09

Support Vector Machine (SVM) Classifier Using WEKA

Aim

To understand and implement support vector machine (svm) classifier in weka using WEKA data mining tool.

Procedure

  1. Open WEKA → Launch Explorer.
  2. Load dataset: Click Open file... → Select iris.arff.
  3. Go to the Classify tab.
  4. Choose classifier:
    • Click Choose → functions → SMO (WEKA's SVM implementation).
  5. Set SVM parameters:
    • Click on the classifier → Configure:
      • Kernel = RBFKernel (default)
      • C = 1.0 (default complexity)
      • Gamma = default or tune based on dataset
  6. Select Test Options:
    • Cross-validation (10 folds) is recommended for evaluation.
    • Or Percentage split (e.g., 70% train, 30% test).
  7. Run classifier: Click Start.
  8. View results:
    • Confusion matrix
    • Classification accuracy
    • Precision, Recall, F1-score

Output

Support Vector Machine (SVM) Classifier Using WEKA output screenshot 1Support Vector Machine (SVM) Classifier Using WEKA output screenshot 2Support Vector Machine (SVM) Classifier Using WEKA output screenshot 3Support Vector Machine (SVM) Classifier Using WEKA output screenshot 4

Result

The experiment was successfully executed, and the expected output was obtained using WEKA.