document: Why collective inference improves relational classification Procedures for collective inference make simultaneous statistical judgments about the same variables for a set of related data instances . For example , collective inference could be used to simultaneously classify a set of hyperlinked documents or infer the legitimacy of a set of related financial transactions . Several recent studies indicate that collective inference can significantly reduce classification error when compared with traditional inference techniques . We investigate the underlying mechanisms for this error reduction by reviewing past work on collective inference and characterizing different types of statistical models used for making inference in relational data . We show important differences among these models , and we characterize the necessary and sufficient conditions for reduced classification error based on experiments with real and simulated data .
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