Although many locality based algorithms show promising performance, there also exists another class of classification algorithms that use all available training data. One such algorithm, Naive Bayes (\tion{nb}), shows a significantly increased probability of detection with an equally increased probability of false detection. Non-local, or global, classifiers are useful in situations where there is too little data to "afford" to discard any. They are also useful in situations where there is little noise. Global classification algorithms often have a mechanism in place to ignore non-relevant data.

Using the whole dataset can provide more information with the classifier to work with. This is useful in datasets with some low frequency class variables\cite{rareClasses}, as the maximum number of training instances are available for classification.
