How to do feature selection with Weka - FAU 2021
Channel: Shaun A Pritchard
Date: 14/10/2021

Advanced Data Mining and Machine Learning

Assignment 3: Feature Selection I CAP6778 FAU

This vide shows how to do feature selection with NaiveBayes in the "bayes" directory) and 5-Nearest Neighbor (IBk in the "lazy" directory, with the KNN parameter changed to 5)

Plus how to install Chi Squared plugin Weka 
Part 1: Preliminary classification
        In preparation for exploring the role of feature selection in classification, you will first build classification models which do not have any built-in feature selection (as is found in the C4.5/J48 decision tree). The two classifiers you will be using are Naïv Bayes


(NaiveBayes in the "bayes" directory) and 5-Nearest Neighbor (IBk in the "lazy" directory, with the KNN parameter changed to 5). Use the full data set to build models with each of these classifiers, and compare their results in terms of the false positive rate, false negative rate, and AUC (area under the ROC curve).



Part 2: Preliminary feature selection
        Here, you will apply feature selection before building your models. Use the Information Gain and Chi Squared feature rankers (InfoGainAttributeEval and ChiSquaredAttributeEval in Weka) to select the top 20 features and save these two new reduced data sets. Then use these data sets with the classifiers in Part 1 to create a total of four (two classifiers x two rankers) new models. Again examine these in terms of their FPR, FNR, and AUC, paying special attention to how these models compare with those built in Part 1.