  We consider the problem of Probably Approximate Correct (PAC) learning of a
binary classifier from noisy labeled examples acquired from multiple annotators
(each characterized by a respective classification noise rate). First, we
consider the complete information scenario, where the learner knows the noise
rates of all the annotators. For this scenario, we derive sample complexity
bound for the Minimum Disagreement Algorithm (MDA) on the number of labeled
examples to be obtained from each annotator. Next, we consider the incomplete
information scenario, where each annotator is strategic and holds the
respective noise rate as a private information. For this scenario, we design a
cost optimal procurement auction mechanism along the lines of Myerson's optimal
auction design framework in a non-trivial manner. This mechanism satisfies
incentive compatibility property, thereby facilitating the learner to elicit
true noise rates of all the annotators.
