CS539 Machine Learning - Spring 2003  Project 8 - Rule Learning

PROF. CAROLINA RUIZ

Due Date: Monday, April 14 2003 at 10 am.

PROJECT DESCRIPTION

Use sequential covering algorithms to construct sets of classification rules for each of the following problems:

1. Predicting the class attribute (CARAVAN Number of mobile home policies) in the The Insurance Company Benchmark (COIL 2000) dataset.

2. Predicting whether the income of a given person is >50K or <= 50K using the census-income dataset from the US Census Bureau which is available at the Univ. of California Irvine Repository.

PROJECT ASSIGNMENT

1. Read Chapter 10 of the textbook on Rule Learning in great detail.

2. The following are guidelines for the construction of your set of rules:

REPORT AND DUE DATE

• Written Report.

Your report should contain the following sections with the corresponding discussions:

1. Code Description: Describe the Prism code that you used from Weka and/or the algorithm implemented in FOIL. Explain the algorithm underlying the code in terms of the input it receives and the output it produces, and the main steps it follows to produce this output.

2. Data: Describe the dataset that you selected in terms of the attributes present in the data, the number of instances, missing values, and other relevant characteristics.

Provide a detail description of the preprocessing of your data. Justify the preprocessing you apply and why the resulting data is the appropriate one for mining neural networks from it.

3. Experiments: For each experiment you ran describe:
• Data: What data did you use to construct and test your classifier?
• Any additional pre or post processing done to the data or the classifier's output in order to improve the accuracy of your classifier.
• Accuracy of the resulting classifier.
• Discuss how this accuracy compares with that of your most accurate ZeroR experiment, decision trees, neural nets, Bayesian methods, instance-based learning, regression methods, and genetic algorithms from the previous assignments.

4. Summary of Results
• What was the accuracy of the most accurate hypothesis (set of rules) you obtained?
• Discuss how this accuracy compares with that of your most accurate results from the previous assignments.
• Include the most accurate hypothesis you obtained in your report.
• Discuss the strengths and the weaknesses of your project.

• Oral Report. We will discuss the results from the individual projects during the class on April 14. Your oral report should summarize the different sections of your written report as described above. Each of you will have 5 minutes to explain your results and to discuss your project in class. Be prepared!

• Submission and Due Date.

Please submit the following files by email to ruiz@cs.wpi.edu by the deadline specified below. Submissions received on Monday April 14, between 10:01 am and 10:45 am will be penalized with 30% off the grade and submissions after 10:45 am won't be accepted.

1. [your-lastname]_proj8_slides.[ext] containing your slides for your oral report. This file should be either a PDF file (ext=pdf) or a PowerPoint file (ext=ppt). Please use only lower case letters in the name file. For instance my file would be named ruiz_proj8_slides.ppt

2. [your-lastname]_proj8_report.pdf containing your written report in PDF.
For instance my file would be named ruiz_proj8_report.pdf ***** ALSO, PLEASE BRING A HARDCOPY OF YOUR REPORT TO CLASS ON APRIL 14, 2003. ****