Compression-based AODE Classifiers
Giorgio Corani, Alessandro Antonucci, D R · Frontiers in artificial intelligence and applications · 2012
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 well-known AODE [10] model. COMP-AODE assumes a uniform prior over the SPODEs; we then develop the credal classifier COMP-AODE*, substituting the uniform prior by a set of priors. COMP-AODE* returns more classes when the classification is prior-dependent, 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.