Compression-based AODE classifiers
Authors: Giorgio Corani, Alessandro Antonucci and Rosa, R.
Abstract: We propose the COMP-AODE classifier, which adopts the compression-based approach to average the posterior probabilities computed by different non-naive classifier (SPODEs). COMP-AODE improves classification performance over the well-known AODE 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.
Year: 2012.
Details: In De Raedt, L. and Bessi{\`e}re, C. and Dubois, D. and Doherty, P. and Frasconi, P. and Heintz, F. and Lucas, P.J.F. (Eds.), Proceedings 20th European Conference on Artificial Intelligence (ECAI 2012). IOS Press, pp. 264-269.
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