Rule-based deduplication utilizes expert domain knowledge to identify and remove duplicate data records. Achieving high accuracy in a rule-based system requires the creation of rules containing a good combination of discriminatory clues. Unfortunately, accurate rule-based deduplication often requires significant manual tuning of both the rules and the corresponding thresholds. This need for manual tuning reduces the efficacy of rule-based deduplication and its applicability to real-world data sets. No adequate solution exists for this problem. We propose a novel technique for rule-based deduplication. We apply individual deduplication rules, and combine the resultant match scores via learning-based information fusion. We show empirically that our fused deduplication technique achieves higher average accuracy than traditional rule-based deduplication. Further, our technique alleviates the need for manual tuning of the deduplication rules and corresponding thresholds.

Existing learning-based multi-modal biometric fusion techniques typically employ a single static Support Vector Machine (SVM). This type of fusion improves the accuracy of biometric classification, but it also has serious limitations because it is based on the assumptions that the set of biometric classifiers to be fused is local, static, and complete. We present a novel multi-SVM approach to multi-modal biometric fusion that addresses the limitations of existing fusion techniques and show empirically that our approach retains good classification accuracy even when some of the biometric modalities are unavailable.