In further sections, I will explore various pre-processing augmentations to the classifiers, but in this section, I are exploring the difference between different locality based and non-locality based classifiers. The locality based classifiers I will explore are LWL, Ripper, Clump, and RIDOR. The non-locality based classifiers are Naive Bayes, C4.5, and OneR. In this section, I will show the performance of the classifiers with the Cross Company and Within Company datasets. I will also show the performance with and without taking the logarithm of the numeric attributes.

The results of the above experiments are shown in \label{pg:results-1}\tbl{results-WC}, \tbl{results-CC}, \tbl{results-WC-Log}, an \tbl{results-CC-Log}. jRip, OneR, C4.5, and Ridor always have the lowest PD's and PF's. This is an equal combination of both locality and non-locality based classifiers. The remaining classifiers, LWL, Clump, and Naive Bayes, are all variations of Naive Bayes. Of these three classifiers, Clump and LWL are locality based classifiers. With the exception of the PF's in \tbl{results-CC}, Clump and Naive Bayes have the statistically highest PD's and PF's. This leaves LWL to be statistically in-between the two groups. In \tbl{results-CC}, Clump, Naive Bayes, and LWL are statistically the same.

Numeric attributes, also known as continuous attributes, pose several problems. One possible problem is an uneven distribution of values within an attributes. When this occurs, applying a logarithmic filter can help to even out the distribution across the whole space of input\cite{distribution1,distribution2,distribution3,locality1}. When comparing \tbl{results-WC-Log}(with log filter) to \tbl{results-WC}(without log filter) and \tbl{results-CC-Log}(with log filter) to \tbl{results-CC}(without log filter), you can see that all of the classifiers have the same, or near the same, PD and PF's. The only exceptions are Naive Bayes and Clump\footnote{As described in \tion{proposed}, the classification algorithm used by Clump is Naive Bayes, so Clump's similarity to Naive Bayes was expected.}. Turhan showed that applying a log filter to the data can help increase the PD's of Naive Bayes drastically\cite{08Turhan}. He also noted that the PF's would rise high enough to make the resulting model unusable in most situations.

Looking at the difference between the Within Company and the Cross Company results, in contrast to prior work\cite{cc, 08Turhan, nn-for-cc}, the PD and PF's show no significant difference. Historically, using Cross-Company data significantly raises both the PD and PF's. This is shown when looking at both the defective and non-defective modules, but not when looking at just the defective modules. I will explore this further in \chap{conclusion}.

If using local data in the classification process improved the classification performance of the locality based classifiers, I would expect to see the majority of the locality based classifiers towards the top of both the PD and the PF charts in \tbl{results-WC} - \tbl{results-CC-Log}. I would also presumably see an increase in PD and decrease in PF in the Within Company tables(\tbl{results-WC} and \tbl{results-WC-Log}) over the Cross Company tables(\tbl{results-CC} and \tbl{results-CC-Log}) because of the increase in non local data seen in the Cross Company tests. This effect is not demonstrated here.

\input{tables/results}
