CSC 573: Data Mining
Weka Assignment #1: Attribute Relevance Analysis in WEKA
Instructor: Ratko Orlandic
For this assignment, your task is to familiarize yourself with the WEKA machine learning tool and
the attribute ranking facilities in WEKA (“Select attributes” feature in WEKA Explorer). For this
assignment, you will use “contact-lenses”, “iris”, and “soybean” data sets, all of which are
available in the required .arff format in the WEKA package. The “contact-lenses” data set has 24
instances with 5 nominal attributes, the last of which (“contact-lenses”) is the class dimension. The
“iris” set has 150 instances with 4 continuous attributes and the nominal class, which is the last
(5th) dimension. The “soybean” set has 683 instances with 36 nominal attributes, the last of which
is the class dimension. Unlike the other two sets, “soybean” has missing values.
Installing WEKA on Your Computer
WEKA machine learning tool is installed on the computers in the computer lab UHB 2030. You
can also download to your computer a free copy of the software as follows:
1. Go to: http://www.cs.waikato.ac.nz/~ml/ .
2. Click on the “software” tab.
3. Under “Getting started”, click on “Download”.
4. Under “Windows”, click on the link to download a self-extracting executable that includes
Java VM 1.4 (weka-3-4-10jre.exe).
5. Install the WEKA software on your computer selecting default directories.
WEKA comes with certain data files and some documentation. Once you install the software, you
can find these on your computer in the directory C:\Program Files\Weka-3-4. Whether you work in
the lab or on your computer, you should spend some time familiarizing yourself with WEKA. For
this assignment, you will be working with WEKA Explorer.
Attribute Relevance Ranking
For each step, open the indicated file in the “Preprocess” window. Then, go to the “Attribute
Selection” window and set the “Attribute selection mode to “Use full training set”. For each case
A-E below, perform attribute ranking using the following attribute selection methods with default
parameters:
a) InfoGainAttributeEval; and
b) GainRatioAttributeEval;
These attribute selection methods should consider only non-class dimensions (for each set, the
class attribute is indicated above the “Start” button). Record the output of each run in a text file
called “output.txt”. For that, copy the output of the run from the “Attribute selection output”
window in the Explorer and paste it at the end of the “output.txt” file.
A. Perform attribute ranking on the “contact-lenses.arff” data set using the two attribute ranking
methods with default parameters.
