Data Mining Methods and Models

258 CHAPTER 6 GENETIC ALGORITHMS
4. Press the Choose button, next to classifier.
5. Select Classifiers → Bayes → Naive Bayes from the navigation hierarchy.
6. Click OK to close the WrapperSubsetEval dialog. The evaluation method for
AttributeSelection is now specified.
7. On the AttributeSelection dialog, press the Choose button next to search.
8. Select AttributeSelection → GeneticSearch from the navigation hierarchy.
9. Press OK to close the AttributeSelection dialog.
The evaluator and search methods for attribute selection have been specified;
however, our changes haven’t yet been applied to our data set.
10. Press the Apply button on the right side of the Explorer panel.
After processing the command, WEKA displays updated results in the Explorer
panel. In particular, under Attributes, notice the list now shows seven predictor attributes.
That is, the two attributes single-cell-size and mitoses have been removed
from the attribute list. Let’s reclassify the records using naive Bayes with 10-fold
cross-validation; however, this time only seven attributes are input to the classifier.
1. Select the Classify Tab.
2. Under Classifier, press the Choose button.
3. Select Classifiers → Bayes → Naive Bayes from the navigation hierarchy.
4. Cross-validation is specified.
5. Click Start.
Now, naive Bayes reports 96.78% (661/683) classification accuracy, which indicates
that the second model outperforms the first model by almost 0.05% (96.78%
versus 96.34%). That is, classification accuracy has increased where only seven of
the nine attributes are specified as input. Although these results do not show a dramatic
improvement in accuracy, this simple example has demonstrated how WEKA’s
Genetic Search algorithm can be included as part of an attribute selection approach.
Let’s further examine the results reported by WEKA’s Genetic Search method,
where the characteristics of the candidate population are described. The following
procedures should look similar to those performed above. This time, however, we’re
invoking the attribute selection filter from WEKA’s Select attributes Tab, which provides
detailed output.
1. Return to the Preprocess Tab on the Explorer panel.
2. Press the Undo button (top right). This removes the filter we applied to the data
set earlier.
3. Select the Select attributes Tab from the Explorer panel.
4. Under Attribute Evaluator, press the Choose button.
5. Select AttributeSelection → WrapperSubsetEval from the navigation hierarchy.
6. Click on the text “WrapperSubsetEval . . . ” to open the WrapperSubsetEval dialog.