Dimensionality Reduction is measured by the portion of candidate-input attributes removed by the algorithm (excluded from the network) and by the size of the information-theoretic network vs. other predictive models.
Prediction Accuracy is the average accuracy of the network on validation cases vs. published accuracy of other classifiers.
Stability represents the ability of the algorithms to provide similar results from different random samples of the same dataset. The benchmark classification methods used for the comparison include:
Naive Bayes Classifier. This is a probabilistic method assuming conditional independence of all input attributes. See the details of the algorithm in (Mitchell, 1997).
C4.5. This is a state-of-the-art decision tree algorithm presented in (Quinlan, 1993). Today, most commercial tools for constructing decision trees are based on C4.5 or one of its modified versions.
CART ™ This is an earlier decision tree method (Breiman et al., 1984). It is used as the engine of a commercial tool, having the same name, which is available from Salford Systems ( http://www.salford-systems.com/ ).
KeywordsDimensionality Reduction Terminal Node Training Error Conditional Entropy Input Attribute
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