This chapter details the experiments that will be conducted.
Without proper experimentation, it is impossible to come to a defensible conclusion. Below I will describe the experimentation procedures I will use, the datasets the experiments will be applied to, and how the results will be evaluated. Just as different classification algorithms will be explored, I will also look into the usage of several pre-processing algorithms. 

\begin{quote}
The goal of these experiments is to evaluate how locality impacts classification performance for software defect prediction.
\end{quote}

First, the experimental procedure and datasets are explained. Next, the algorithms are individually analyzed, with special attention being paid to Locally Weighed Naive Bayes. Third, relevancy filtering is explored by applying the Burak filter. Finally, the original experiments, augmented with various clustering algorithms, are re-run. The results of these experiments will be explored in \chap{results}. 
