We present abstraction augmented Markov models (AAMMs), which are directed acyclic graphical models that simplify the data representation used by the standard entities that are organized in an abstraction hierarchy. Abstraction reduces the MM size and improves the statistical estimates of complex models by reducing the number of parameters to be estimated from data. We evaluate the AAMMs on two protein subcellular localization prediction tasks. The results of our experiments show that: (1) AAMMs can achieve significantly lower model sizes (by 1 to 3 orders of magnitude) for a minor drop in accuracy over the standard MMs, and in some cases even higher accuracy while simultaneously lowering the model size; and (2) AAMMs substantially outperforms MMs in settings where only a small fraction of available data is labeled
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