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In this paper, we test machine learning methods for results merging in patentdocument retrieval. Specifically, we examine random forest, decision tree,support vector machine (SVR), linear regression, polynomial regression, anddeep neural networks (DNNs). We use two different methods for results merging,the multiple models (MM) method and the global model method (GM). Furthermore,we examine whether the ranking of the document's scores is linearlyexplainable. The CLEF-IP 2011 standard test collection was used in ourexperiments. The random forest produces the best results in comparison to allother models, and it fits the data better than linear and polynomialapproaches.

Results Merging in the Patent Domain