Majority voting is a popular and robust strategy to aggregate different opinions in learning from crowds, where each worker labels examples ac- cording to their own criteria. Although it has been extensively studied in the binary case, its behavior with multiple classes is not completely clear, specifically when annotations are biased. This paper attempts to fill that gap. The behavior of the majority voting strategy is studied in-depth in multi-class domains, emphasizing the effect of annotation bias. By means of a complete experimental setting, we show the limitations of the stan- dard majority voting strategy. The use of three simple techniques that infer global information from the annotations and annotators allows us to put the performance of the majority voting strategy in context.

TIN2016-78365-R