This tutorial is the continuation of the preceding one about the wrapper feature selection in the supervised learning context (http://data-mining-tutorials.blogspot.com/2010/03/wrapper-for-feature-selection.html). We analyzed the behavior of Sipina, and we have described the source code for the wrapper process (forward search) under R (http://www.r-project.org/). Now, we show the utilization of the same principle under Knime 2.1.1, Weka 3.6.0 and RapidMiner 4.6.
The approach is as follows: (1) we use the training set for the selection of the most relevant variables for classification; (2) we learn the model on selected descriptors; (3) we assess the performance on a test set containing all the descriptors.
This third point is very important. We cannot know the variables that will be finally selected. We do not have to manually prepare the test file by including only those which have been selected by the wrapper procedure. This is essential for the automation of the process. Indeed, otherwise, each change of setting in the wrapper procedure leading to another subset of descriptors would require us to manually edit the test file. This is very tedious.
In the light of this specification, it appeared that only Knime was able to implement the complete process. With the other tools, it is possible to select the relevant variables on the training file. But, I could not (or I did not know) apply the model on a test file containing all the original variables.
The naive bayes classifier is the learning method used in this tutorial .
Keywords: feature selection, supervised learning, naive bayes classifier, wrapper, knime, weka, rapidminer
Tutorial: en_Tanagra_Wrapper_Continued.pdf
Dataset: mushroom_wrapper.zip
References :
JMLR Special Issue on Variable and Feature Selection - 2003
R Kohavi, G. John, « The wrapper approach », 1997.
Wikipedia, "Naive bayes classifier".
