  This paper designs an efficient two-class pattern classifier utilizing
asynchronous cellular automata (ACAs). The two-state three-neighborhood
one-dimensional ACAs that converge to fixed points from arbitrary seeds are
used here for pattern classification. To design the classifier, we first
identify a set of ACAs that always converge to fixed points from any seeds with
following properties - (1) each ACA should have at least two but not huge
number of fixed point attractors, and (2) the convergence time of these ACAs
are not to be exponential. In order to address the first issue, we propose a
graph, coined as fixed point graph of an ACA that facilitates in counting the
fixed points. We further perform an experimental study to estimate the
convergence time of ACAs, and find that there are some convergent ACAs which
demand exponential convergence time. Finally, we find that there are 71 (out of
256) ACAs which can be effective candidates as pattern classifier. We use each
of the candidate ACAs on some standard data sets, and observe the effectiveness
of each ACAs as pattern classifier. It is observed that the proposed classifier
is very competitive and performs reliably better than many standard existing
algorithms.
