Presentation on the Rule_ Based_ Classifier

Rule based classifier .

1. Rule-Based Classifier

2. Introduction Definition: Classifier that uses IF–THEN rules for classification. Example: IF (Age < 30 AND Income = High) THEN Class = 'Yes'

3. Features of Rule-Based Classifiers - Human-readable & interpretable - Handles categorical and numerical data - Flexible & modular - Easy explanation for decision-making

4. Structure of Rules General form: IF (Condition) THEN (Class) - Conditions: attributes, thresholds, or categorical values - Class: output label

5. Rule Generation Approaches - Direct methods: from data (e.g., decision tree → rules) - Indirect methods: from association rule mining - Manual knowledge-based rules

6. Example Dataset & Rules Dataset: Attributes = Age, Salary, Buy Rules: - IF Age < 25 AND Salary = Low THEN Buy = No - IF Age > 40 AND Salary = High THEN Buy = Yes

7. Rule Ordering Strategies - Sequential covering: remove covered records - Rule prioritization: accuracy, coverage, confidence - Default Rule: applied when no rule fires

8. Metrics for Evaluation - Accuracy: Correctly classified / Total - Coverage: Portion of instances rule applies to - Precision, Recall, F1-score

9. Advantages - Transparent & interpretable - Suitable for domains needing explanations - Easy integration with expert knowledge

10. Limitations - Can overfit with too many rules - Conflicting rules → ambiguity - Not efficient for very large datasets

11. Applications - Medical diagnosis - Credit scoring - Fraud detection - Customer classification

12. Visualization Rule flow diagram (example decision path)

13. Conclusion - Rule-based classifiers = Simple yet powerful - Best for interpretable models - Used widely in decision-support systems

14. References - Han, J., Kamber, M., & Pei, J. (2012). Data Mining: Concepts and Techniques - Mitchell, T. M. (1997). Machine Learning