  In this paper, we present a novel sequential paradigm for classification in
crowdsourcing systems. Considering that workers are unreliable and they perform
the tests with errors, we study the construction of decision trees so as to
minimize the probability of mis-classification. By exploiting the connection
between probability of mis-classification and entropy at each level of the
decision tree, we propose two algorithms for decision tree design. Furthermore,
the worker assignment problem is studied when workers can be assigned to
different tests of the decision tree to provide a trade-off between
classification cost and resulting error performance. Numerical results are
presented for illustration.
