CS 539 Spring 2003

Computer Science Department

CS539 Machine Learning - Spring 2003
Project 6 - Instance-Based Learning and Regression Methods

PROF. CAROLINA RUIZ

Due Date: Thursday, March 20 2003 at 8 am.

Project Description
Project Assignment
Report Submission and Due Date

PROJECT DESCRIPTION

Use Instance-based Learning and Regression techniques 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 a numeric attribute of your choice 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.
Predicting a numeric attribute of your choice in the census-income dataset.

PROJECT ASSIGNMENT

Read Chapter 8 of the textbook about Instance-based Learning in great detail.

Read the code of the Instance-based Learning and Regression techiques implemented in the Weka system. Some of those techniques are enumerated below:

Instance-based Learning:
- IB1: nearest neighbor classification
- IBk: k-nearest neighbors classification
Regression:
- Classification via Regression
- Linear Regression
- LWR: Locally Weighted Regression
- Additive Regression (optional)
- Regression by Discretization

The following are guidelines for the construction of your Instance-based and Regression Classifiers:
- 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 Weka cannot handle your whole dataset (this is unlikely). But remember that the more accurate your neural network 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. 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).
- Evaluation and Testing: As usual, experiment with different testing methods:
  1. Supply separate training and testing data to Weka.
  2. Supply training data to Weka and experiment with several split ratios.
  3. Supply training data to Weka and use n-fold crossvalidation to test your results. Experiment with different values for the number of folds.

REPORT AND DUE DATE

Written Report.
Your report should contain the following sections with the corresponding discussions:
1. Code Description: Explain the algorithm underlying the code for each of the Weka techniques you use in terms of the input it receives, the output it produces, and the main steps it follows to produce this output.
  Explain the differences among the approaches you use, as much as possible.
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 these classifications models 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, neural nets, and Bayesian techniques from the previous assignments.
4. 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 20. 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 Mondays, between 8:01 am and 10:00 am will be penalized with 30% off the grade and submissions after 10:00 am won't be accepted.
1. [your-lastname]_proj6_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_proj6_slides.ppt
2. [your-lastname]_proj6_report.pdf containing your written report in PDF.
  For instance my file would be named ruiz_proj6_report.pdf ***** ALSO, PLEASE BRING A HARDCOPY OF YOUR REPORT TO CLASS ON MARCH 20, 2003. ****

CS539 Machine Learning - Spring 2003 Project 6 - Instance-Based Learning and Regression Methods

PROF. CAROLINA RUIZ

PROJECT DESCRIPTION

PROJECT ASSIGNMENT

REPORT AND DUE DATE

CS539 Machine Learning - Spring 2003
Project 6 - Instance-Based Learning and Regression Methods