CS 539 Fall 2000 - Assignment 9

Computer Science Department

CS539 Machine Learning
Assignment 9 - Fall 2000

PROF. CAROLINA RUIZ

Due: Thursday, November 16, 2000 at 6:00 pm.

Project Description
Project Assignment
Report Submission and Due Date

PROJECT DESCRIPTION

Use genetic algorithms to construct the most accurate hypothesis you can for 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.

I have downloaded the dataset into the following directory: /cs/courses/cs539/f00/Projects/Census_Income_Data
You can access the dataset from there.

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

Construct, using a genetic algorithm, the most accurate hypothesis you can to predict the Salary attribute of the Census-Income data. The following are guidelines to use genetic algorithms to construct your hypothesis:

Code: You can use a genetic algorithm available online as long as it implements crossover, mutation, and selection as described in Chapter 9 of the textbook. You'll receive extra-credit points for developing your own implementation of the algorithm.

Decisions, decisions, decisions: You can choose the strategy and parameters of your algorithm as you prefer. Section 9.3 provides a description of a very nice approach to using genetic algorithms to construct a classifier. I suggest you follow this approach, but you can follow a different one if you wish.

You should define the following notions/parameters for your system (make sure you explain your choices clearly in your written report): individuals (i.e. hypotheses), population, fitness, size of the population, fitness threshold, mutation rate, and fraction of population replaced by crossover at each step, among others.

You should also explain how you measure "accuracy" in your system.

Instances: Use the census-income dataset. 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. YOU MUST USE AT LEAST THE FIRST 1000 TEST RECORDS FROM THE Test data IN YOUR EXPERIMENTS.

REPORT AND DUE DATE

Written Report. Please bring your report to my office (FL232) or to class by the due date/time. Your report should contain the following sections that discuss the issues:
1. Code Description: Describe the code that you used/wrote. 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 net.
  - 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 system.
Oral Report. We will discuss the results from the individual projects during the class on November 16th. Be ready to show your results (prepare transparencies on your results) and to discuss your project solution in class.

CS539 Machine Learning Assignment 9 - Fall 2000

PROF. CAROLINA RUIZ

PROJECT DESCRIPTION

PROJECT ASSIGNMENT

REPORT AND DUE DATE

CS539 Machine Learning
Assignment 9 - Fall 2000