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 ...
