Reclassification of linearly classified data using constraint databases

Abstract. In many problems the raw data is already classified according to a variety of features using some linear classification algorithm but needs to be reclassified. We introduce a novel reclassification method that creates new classes by combining in a flexible way the existing classes without requiring access to the raw data. The flexibility is achieved by representing the results of the linear classifications in a linear constraint database and using the full query capabilities of a constraint database system. We implemented this method based on the MLPQ constraint database system. We also tested the method on a data that was already classified using a decision tree algorithm.