PROJECT DESCRIPTION

PROJECT ASSIGNMENT

• Code: You can use the decision tree methods implemented in the Weka system. I recommend using ID3 for your experiments.

• Training and Testing Instances:

  You may restrict your experiments to a subset of the dataset IF Weka cannot handle your whole dataset (this is unlikely). But remember that the more accurate your system is, the better.

  A main part of this project is the PREPROCESSING of your dataset. You must apply relevant concept hierarchies and generalizations to your dataset before doing the mining and/or using the results of previous mining tasks (e.g. project 2, initial minings of the data using ID3). Your report should contained a detailed description of the preprocessing of your dataset and justifications of the steps you followed. If Weka does not provide the functionality you need to preprocess your data as you need to obtain useful patterns, preprocess the data yourself either by writing the necessary filters (you can incorporate them in Weka if you wish).

  Use n-fold crossvalidation to test your results. I recommend using n=4, but you may use a different value IF needed given your dataset (you'll need to justify a different selection in your report).

REPORT AND DUE DATE

• Written Report. Your written report is due at 3:00 pm. Please hand it in at the beginning of class. Your report should contain the following sections with the corresponding disc ussions:

  1. Code Description: Describe the decision tree code that you used from Weka. Explain the algorithm underlying the code in terms of the input it receives and the output it produces, and the 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 decision trees from it.

  3. Experiments: For each experiment you ran describe:
     • Data: What data did you use to construct and test your decision tree?
     • Any additional pre or post processing done to improve the accuracy of your tree.
     • Accuracy of the resulting decision tree.

  4. Summary of Results
     • What was the accuracy of the most accurate decision tree constructed by your system?
     • Include the most accurate tree you obtained in your report.
     • strengths and the weaknesses of your system.

• Oral Report. We will discuss the results from the individual projects during the class on March 30th 2004. Be ready to show your results and to discuss your project in 7 minutes durin class. PREPARE SLIDES SHOWING YOUR WORK.