Moran, Stuart; He, Yulan and Liu, Kecheng
Choosing the best Bayesian classifier: an empirical study.
IAENG International Journal of Computer Science, 36(4) pp. 322–331.
It is often difficult for data miners to know which classifier will perform most effectively in any given dataset. Usually an understanding of learning algorithms is combined with detailed domain knowledge of the dataset at hand to lead to the choice of a classifier. We propose an empirical framework that quantitatively assesses the accuracy of a selection of classifiers on different datasets, resulting in a set of classification rules generated by the J48 decision tree algorithm. Data miners can follow these rules to select the most effective classifier for their work. By optimising the parameters used for learning, a set of rules were learned that select with 78% accuracy (with 0.5% classification accuracy tolerance), the most effective classifier.
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