  Record fusion is the task of aggregating multiple records that correspond to
the same real-world entity in a database. We can view record fusion as a
machine learning problem where the goal is to predict the "correct" value for
each attribute for each entity. Given a database, we use a combination of
attribute-level, recordlevel, and database-level signals to construct a feature
vector for each cell (or (row, col)) of that database. We use this feature
vector alongwith the ground-truth information to learn a classifier for each of
the attributes of the database.
  Our learning algorithm uses a novel stagewise additive model. At each stage,
we construct a new feature vector by combining a part of the original feature
vector with features computed by the predictions from the previous stage. We
then learn a softmax classifier over the new feature space. This greedy
stagewise approach can be viewed as a deep model where at each stage, we are
adding more complicated non-linear transformations of the original feature
vector. We show that our approach fuses records with an average precision of
~98% when source information of records is available, and ~94% without source
information across a diverse array of real-world datasets. We compare our
approach to a comprehensive collection of data fusion and entity consolidation
methods considered in the literature. We show that our approach can achieve an
average precision improvement of ~20%/~45% with/without source information
respectively.
