SPPAM - Statistical PreProcessing AlgorithM
  Most machine learning tools work with a single table where each row is an
instance and each column is an attribute. Each cell of the table contains an
attribute value for an instance. This representation prevents one important
form of learning, which is, classification based on groups of correlated
records, such as multiple exams of a single patient, internet customer
preferences, weather forecast or prediction of sea conditions for a given day.
To some extent, relational learning methods, such as inductive logic
programming, can capture this correlation through the use of intensional
predicates added to the background knowledge. In this work, we propose SPPAM,
an algorithm that aggregates past observations in one single record. We show
that applying SPPAM to the original correlated data, before the learning task,
can produce classifiers that are better than the ones trained using all
records.
