We will now look at how to perform attribute selection. Attribute selection is a technique for deciding which attributes are the most favorable attributes for performing classification or clustering.

So, let's take a look at the code and see what happens, as follows:

import weka.core.Instances;
import weka.core.converters.ArffSaver;
import java.io.File;
import weka.core.converters.ConverterUtils.DataSource;
import weka.filters.Filter;
import weka.filters.supervised.attribute.AttributeSelection;
import weka.attributeSelection.CfsSubsetEval;
import weka.attributeSelection.GreedyStepwise;

The first five classes will be the same as those we used earlier. We will also be using a new type of attribute, which will be a supervised attribute from the filters.supervised package, and the AttributeSelection class. Then, we have an attribute.Selection package, and from that, we'll be using the CfsSubsetEval class and the GreedyStepwise class.

In the following code, we'll first read the...