• Solve Exercises 6.1 and 6.6 of your textbook (pages 198-199). Include your solution in your written report (and not in your oral report).

*** NEW ***: Your written report should be at most 10 pages long (including the homework solutions, and all the graphs, figures, and appendices). The font size should be no smaller than 11pts.

• Data Mining Technique(s): Use the Naive Bayes and Bayesian Net classifiers methods implemented in the Weka system, or implement your own code.

• Dataset(s): In this project, we will use two datasets:

  • (140 points. See Project 5 Grading Sheet.)
    The Labor Relations Data Set available in Weka and also at the UCI Machine Learning Repository.

  • (270 points. See Project 5 Grading Sheet.)
    The Spambase Data Set available at the UCI Machine Learning Repository.

• Performance Metric(s):
  • Use classification accuracy. If you wish, you can use other metrics to evaluate the "goodness" of your models, IN ADDITION to accuracy.
  • If possible, compare the accuracies you obtained against those of benchmarking techniques or previously studied techniques as ZeroR, OneR, ID3, J4.8, ANNs over the same (sub-)set of data instances you used in each experiment.
  • Report the training time needed to construct the model in each of the experiments.

• Algorithm Options:

  • Naive Bayes: Run experiments with and without the supervised discretization option. Contrast the results obtained. Also, experiment with and without Feature Selection.

  • 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. Also, experiment with and without Feature Selection. 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.