Locality based learners fall into two main categories: rule based, and instance based classifiers. The rule based classifiers create one set of rules during training, and use the generated rules during the testing process. Instance based classifiers re-train for each testing instance, ostensibly creating a new cluster for each testing instance. Both types of localized learners attempt to improve classification performance by reducing noise, and emphasize the relevant training instances during testing.

Localization benefits classification by reducing noise. A common use is when training data comes from a public or unpredictable source. By only using the testing instances close to the training instance, non relevant instances, including any erroneous instances, are ignored. This serves to emphasize the relevant instances, and should increase classification performance. Jiang et al.\cite{Jiang} compares Locally Weighted Naive Bayes(LWL) and Naive Bayes in an effort to show locality can improve classification performance. The experiments were run on 36 datasets from the UCI repository\cite{UCI} recommended by Weka\cite{weka}. In his experiments, he shows that LWL performs statistically better in 11 out of 36 datasets, performs statistically the same in 20 out of 36 datasets, and performs statistically worse in 5. In \chap{results}, I will show that locality does not increase the performance of classification when it is used for software defect prediction.
