CS 539 Spring 2005

Computer Science Department

CS539 Machine Learning - Spring 2005
Project 5 - Bayesian Learning

PROF. CAROLINA RUIZ

Due Date: Tuesday, March 15 2005. Slides are due at 3:00 pm and the written report is due at 4:00 pm.

Project Description
Project Assignment
Report Submission and Due Date

PROJECT DESCRIPTION

Experiment with Naive Bayes and Bayesian Net classifiers for each of the following problems:

Predicting the class attribute in the Covertype data available at the UCI Machine Learning Repository.
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.
The census-income dataset contains census information for 48,842 people. It has 14 attributes for each person (age, workclass, fnlwgt, education, education-num, marital-status, occupation, relationship, race, sex, capital-gain, capital-loss, hours-per-week, and native-country) and a boolean attribute class classifying the input of the person as belonging to one of two categories >50K, <=50K.

PROJECT ASSIGNMENT

Read Sections 6.1, 6.2, 6.7, 6.8, 6.9, 6.10, 6.11, 6.12, 6.13 of your textbook in great detail.

Read the NaiveBayes and the BayesNets code in the Weka system.

The following are guidelines for the construction of your Naive Bayes and Bayesian Net Classifiers:
- Algorithm Options:
  
  Naive Bayes: Run experiments with and without the supervised discretization option. Contrast the results obtained.
  Bayesian Nets: Run experiments with different search methods (including at least K2 and HillClimber), different upper bounds on the number of parents, and different orderings of the predicting attributes. For a description of the K2 algorithm see G. F. Cooper & E. Herskovits. "A Bayesian Method for the Induction of Probabilistic Networks from Data". For an illustration of the K2 algorithm over the toy dataset in that paper, see Prof. Ruiz's K2 handout
- Evaluation and Testing: Use an evaluation protocol that you find appropriate for this task. Alternatives are split percentage and n-fold cross-validation.
- Comparison of the Algorithms:
- Use stratified sampling to select a subset S of 1000 data instances from the Cover Type dataset. Piotr's script can be used for this purpose - thanks Piotr!
  1. Learn a Naive Bayes model m with the best parameters settings you can over S using a 75% split. That is, use 75% of the data in S to build the model and the remaining 25% to calculate the error_S'(m). Use this error_S'(m) to estimate with 95% probability the error_D(m), i.e. the error of t over the entire distribution D of cover type instances.
  2. Learn a Bayesian network Bn (with the best parameters settings you can) with 1 hidden layer and other default parameters over the dataset S using a 75% split. That is, use 75% of the data in S to build the net and the remaining 25% to calculate the error_S'(Bn). Compare the Naive Bayes model m from above and the Bayesian network Bn by estimating the difference d between the true errors of these two hypotheses with 95% probability using error_S'(t) and error_S'(Bn).
  3. Compare Naive Bayes and Bayesian Nets over the Cover Type dataset by estimating the difference in error between these two learning methods over the Cover Type dataset with an approximate confidence interval of 95%. Do this by using a paired t test with k=11 with the data subset S from above as D₀.

REPORT AND DUE DATE

Written Report.
Your report should contain the following sections with the corresponding discussions:
1. Algorithm Description: Describe IN YOUR OWN WORDS the Naive Bayes algorithm and the K2 Bayesian Net learning algorithm. Explain each algorithm in terms of the input it receives, the output it produces, and the main steps it follows to produce this output from the input.
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, and neural nets from the previous assignments over the CoverType data.
4. Comparison of the Algorithms: Include the results of the comparisons between the approximate errors of Naive Bayes and Bayesian Nets over the CoverType data as described above.
5. Summary of Results
  - For each dataset, what was the accuracy of the most accurate classifier constructed in your project?
  - strengths and the weaknesses of your project.
Oral Report. We will discuss the results from the individual projects during the class on March 10. Your oral report should summarize the different sections of your written report as described above. Each of you will have 7 minutes to explain your results and to discuss your project in class. Be prepared!
Submission and Due Date.
1. Please submit the following file by email by Tuesday March 15th at 3:00 pm.
2. [your-lastname]_proj5_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_proj5_slides.ppt
3. Please bring a hardcopy of your report to class on March 15, 2005.

CS539 Machine Learning - Spring 2005 Project 5 - Bayesian Learning

PROF. CAROLINA RUIZ

PROJECT DESCRIPTION

PROJECT ASSIGNMENT

REPORT AND DUE DATE

CS539 Machine Learning - Spring 2005
Project 5 - Bayesian Learning