CS 539 Spring 2003

Computer Science Department

CS539 Machine Learning - Spring 2003
Project 7 - Genetic Algorithms

PROF. CAROLINA RUIZ

Due Date: Thursday, April 03 2003 at 10 am.

Project Description
Project Assignment
Report Submission and Due Date

PROJECT DESCRIPTION

Use Genetic Algorithms to construct classifiers for each of the following problems:

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

Read Chapter 9 of the textbook on Genetic Algorithms in great detail.

The following are guidelines to use genetic algorithms to construct your hypothesis:
- Training and Testing Instances:
  
  For the The Insurance Company Benchmark, use the ticdata2000.txt data for training and the ticeval2000.txt data for testing. You may restrict your experiments to a subset of the instances IF your system cannot handle your whole dataset. But remember that the more accurate your classifier is, the better.
  For the Census Income data, use the census-income dataset for training. You can restrict your experiments to a subset of the dataset if your system cannot handle the whole dataset. But remember that the more accurate your system is, the better. Also, note that this dataset has missing values. It is up to you how to fill in appropriate data for those missing values. Also, it is up to you to decide if it's a good idea to discretize continues attributes, and if so, how.
  Note that Test data are also available at the UCI. YOU MUST USE AT LEAST THE FIRST 1000 TEST RECORDS FROM THAT TEST DATA IN YOUR EXPERIMENTS.
- Preprocessing of the Data: You should apply relevant filters to your dataset as needed before doing the mining and/or using the results of previous mining tasks. For instance, you may decide to remove apparently irrelevant attributes, replace missing values if any, discretize attributes in a different way, etc. Your report should contained a detailed description of the preprocessing of your dataset and justifications of the steps you followed.

REPORT AND DUE DATE

Written Report.
Your report should contain the following sections with the corresponding discussions:
1. Code Description: Describe the code that you used/wrote. In particular, describe the algorithm implemented by that code. Remember to acknowledge any sources of information/code you used for the implementation of your system.
2. Experiments: For each experiment you ran describe:
  - Your system parameters: individuals (i.e. hypotheses), initial population, definition of fitness, size of the population, fitness threshold, mutation rate, and fraction of population replaced by crossover at each step, etc.
  - Instances: What data did you use to evaluate the fitness of the individuals in your population?
  - Any pre or post processing done to improve the accuracy of your classifier.
  - Accuracy of the resulting hypothesis. Remember to explain how you measure accuracy in your system.
3. Summary of Results
  - What was the accuracy of the most accurate hypothesis 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 03. 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 Thursday April 03, 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]_proj7_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_proj7_slides.ppt
2. [your-lastname]_proj7_report.pdf containing your written report in PDF.
  For instance my file would be named ruiz_proj7_report.pdf ***** ALSO, PLEASE BRING A HARDCOPY OF YOUR REPORT TO CLASS ON APRIL 03, 2003. ****

CS539 Machine Learning - Spring 2003 Project 7 - Genetic Algorithms

PROF. CAROLINA RUIZ

PROJECT DESCRIPTION

PROJECT ASSIGNMENT

REPORT AND DUE DATE

CS539 Machine Learning - Spring 2003
Project 7 - Genetic Algorithms