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In this paper we explore the effectiveness of combining diverse machine learning based methods to categorize patent applications. Classifiers are constructed from each categorization method in the combination, based on the document representations where the best performance was obtained. Therefore, the ensemble of methods makes categorization predictions with knowledge observed from different perspectives. In addition, we explore the application of a variety of combination techniques to improve the overall performance of the ensemble of classifiers. In our experiments a refined version of the WIPO-alpha document collection was used to train and evaluate the classifiers. The combination ensemble that achieved the best performance obtained an improvement of 6.51% compared to the best performing classifier participating in the combination.

Automatic Categorization of Patent Applications Using Classifier Combinations