Selecting features/attributes using the low-level method, the filtering method, and the meta-classifier method

Feature selection is an important machine-learning process that identifies the most important attributes in a dataset from a set of attributes, so that if a classifier is generated based on the selected attributes, the classifier produces better results than the one with all the attributes.

In Weka, there are three ways of selecting attributes. This recipe will use all of the three ways of attribute selection techniques available in Weka: the low-level attribute selection method, attribute selection using a filter, and attribute selection using a meta-classifier.

Getting ready

The recipe will select important attributes of the iris dataset ...

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