To create a shortlist of suitable classifiers for further refining, we ran the classifiers on the same data to compare them.

We chose to test 6 different classifiers for a spread of different approaches to classifying.

We tested them both with and without SMOTE preprocessing.

The following images show a comparison of the kappa and accuracy scores of the six classifiers. Kappa can be seen as a single value measure of the amount of true and false positives and negatives. A kappa closer to 1 is better, a kappa below 0.4 can be seen as poor. These guidelines are relatively arbitrary.

NaiveBayes is an implementation of a probabilistic classifier.

IBk is an implementation of a lazy learning method.

JRip is an implementation of a rule based method.

J48 is an implementation of a decision tree based method

SMO and LibSVM are implementation of a support vector machine method, with LibSVM implementing SMO.