Attribute selection

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...