Compression-based AODE Classifiers

We propose the COMP-AODE classifier, which adopts the compression-based approach [1] to average the posterior probabilities computed by different non-naive classifiers (SPODEs). COMP-AODE improves classification performance over the wellknown AODE [10] model. COMP-AODE assumes a uniform prior over the SPODEs; we then develop the credal classifier COMPAODE*, substituting the uniform prior by a set of priors. COMPAODE* returns more classes when the classification is priordependent, namely if the most probable class varies with the prior adopted over the SPODEs. COMP-AODE* achieves higher classification utility than both COMP-AODE and AODE.