We propose a technique for pattern classification in symbolic streams via
selective erasure of observed symbols, in cases where the patterns of interest
are represented as Probabilistic Finite State Automata (PFSA). We define an
additive abelian group for a slightly restricted subset of probabilistic finite
state automata (PFSA), and the group sum is used to formulate pattern-specific
semantic annihilators. The annihilators attempt to identify pre-specified
patterns via removal of essentially all inter-symbol correlations from observed
sequences, thereby turning them into symbolic white noise. Thus a perfect
annihilation corresponds to a perfect pattern match. This approach of
classification via information annihilation is shown to be strictly
advantageous, with theoretical guarantees, for a large class of PFSA models.
The results are supported by simulation experiments.