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Businesses, governmental bodies and NGO's have an ever-increasing amount ofdata at their disposal from which they try to extract valuable information.Often, this needs to be done not only accurately but also within a short timeframe. Clean and consistent data is therefore crucial. Data matching is thefield that tries to identify instances in data that refer to the samereal-world entity. In this study, machine learning techniques are combined withstring similarity functions to the field of data matching. A dataset ofinvoices from a variety of businesses and organizations was preprocessed with agrouping scheme to reduce pair dimensionality and a set of similarity functionswas used to quantify similarity between invoice pairs. The resulting invoicepair dataset was then used to train and validate a neural network and a boosteddecision tree. The performance was compared with a solution from FISCALTechnologies as a benchmark against currently available deduplicationsolutions. Both the neural network and boosted decision tree showed equal tobetter performance.

Supervised machine learning techniques for data matching based on similarity metrics