Lecture on Classification Rule Process using J48 Algorithm

Overview

• Objective: Demonstrate the classification process using the J48 algorithm on a dataset.
• Dataset: student.arff.
• Algorithm: J48, an extension of ID3 algorithm, also known as C4.5.
• Outcome: Construct a decision tree structure to classify data.

Key Concepts

• ID3 vs. C4.5: J48 is an implementation of C4.5, which is an extension of ID3.
• Decision Tree: Tree-like structure used to make decisions based on data attributes.

Steps to Create a Dataset (student.arff)

1. Create a Text File: Open a new text file and save as .arff format.
2. Define Attributes: Add attributes with @attribute keyword.
   • Attributes: age, income, student, credit_card_rating, buying_computer
   • Example values for age: <30, 30-40, >40
   • Example values for income: low, medium, high
3. Add Data: Define data with @data keyword and enter values separated by commas.
   • Example record: <30, high, yes, fair, no

Process of Classification Using J48

1. Open Application: Load student.arff file in the classification software.
2. Pre-process Data: Check for and handle any missing values in attributes.
3. Select Classification Algorithm: Choose J48 from the list of algorithms under the trees section.
4. Run Algorithm: Hit the start button to begin the classification process.
5. View Results: Analyze the outputs such as correctly classified instances, incorrectly classified instances, and decision tree visualization.

Important Observations (Example Dataset)

• For age < 30: Not able to buy a computer.
• For age 30-40: Able to buy a computer.
• For age > 40: Check credit_card_rating.
  • If fair, able to buy a computer.
  • If excellent, not able to buy a computer.

Statistical Metrics

• Correctly Classified Instances: 78%
• Incorrectly Classified Instances: 21%
• Confusion Matrix and other error rates (Mean Absolute Error, Root Mean Squared Error) are also provided.

Conclusion

• The decision tree generated by the J48 algorithm helps in predicting the class labels based on the input attributes.
• This method can be used to make predictions on new, unseen data based on the trained model's structure.

Practical Applications

• Example: Given a person's age, income, student status, and credit card rating, we can predict whether they will buy a computer or not.
• Tree Interpretation: Helps in understanding the factors influencing the decision-making process.

Steps to Complete the Experiment

1. Dataset Preparation: Create and save .arff file with attributes and data.
2. Load Dataset: Into classification software and pre-process if required.
3. Algorithm Selection: Choose J48 and run it on the dataset.
4. Results Analysis: Note down the observations, decision tree, and performance metrics.
5. Documentation: Write down the step-by-step process and observations in your experiment report.