  We study the arbitrary cost case of the unweighted Stochastic Score
Classification (SSClass) problem. We show two constant approximation algorithms
and both algorithms are 6-approximation non-adaptive algorithms with respect to
the optimal adaptive algorithm. The first algorithm uses a modified round-robin
approach among three sequences, which is inspired by a recent result on the
unit cost case of the SSClass problem. The second algorithm is originally from
the work of Gkenosis et al. In our work, we successfully improve its
approximation factor from 2(B-1) to 6. Our analysis heavily uses the relation
between computation and verification of functions, which was studied in the
information theory literature.
