Boosting in the Presence of Missing Data

The learning approaches presented in the last two chapters employed the closed-world assumption i.e., whatever that is not observed in the data is assumed to be false. In this chapter, we relax this assumption and derive a boosting algorithm that can effectively work with missing data. The derivation is independent of the model and hence we will discuss about adapting it for RDNs and MLNs. As with other chapters, we will conclude with empirical evaluation on the SRL data sets.