## 1 Introduction  
The analysis of patents is one of the core duties of technology and innovation management with varying purposes and perspectives, such as forecasting emerging technologies, assessing performances of regional/national innovation systems, mapping technologies, managing R&D activities, or evaluating the collaboration potential at company or policy level [1, 2]. An essential subtask within these processes is classifying patents into coherent groups of similar documents as base for further retrieval and assessment. To facilitate such approaches, examiners at the national authorities use official taxonomies to assign patent applications according to their content to one or more classes. To overcome the so far existing separation, in 2013 the European Patent Office (EPO) and the United States Patent Office (USTPO) jointly released the Cooperative Patent Classification (CPC), which compared to the previously used International Patent Classification (IPC) now comprises about four times the number of different classes [3]. However, in order to assess domain-specific emerging technologies, either the use of concordance tools/tables to map, e.g., medical technologies [4] or a complete reclassification of patents beyond the official taxonomy systems is still indispensable [1]. Facing the huge and still expanding number of worldwide patents [5], the implementation of automated text categorization methods becomes vital to keep up with the rapidly growing body of knowledge. Many approaches have been deployed differing in features, such as Machine Learning (ML) algorithms, domains etc. Most of these approaches use the bag-of-word model to transform the text into term frequencies as real-valued features for machine learning (Term Frequency-Inverted Document Frequency--TF-IDF) [6]. In order to improve the classification performance, advanced methods incorporate more and more (meta) data of several distinct sources [7]. However, the subsequently increasing complexity raises new difficulties in mastering both varying methods and different data sources, such as data accessibility and integration (deduplication), the number of features in sparse matrices, the need of a method to combine multiple predictions, or the necessary computing power. Surprisingly, only a few studies implement IPC, and even less use the CPC assignments as an additional information source, although the data are readily available jointly with patent texts. Furthermore, the well-studied fusion of different classifiers promises improved performance results especially when the diversity of the base classifiers is high [8]. A plethora of fusion methods, e.g., rule-based approaches such as summing and averaging, or stacking with machine leaning algorithms acting as fusion classifiers, are available to customize ensemble classification systems to specific tasks. Despite of these advantages, so far patent categorization into user-defined groups via ensemble classifier systems has rarely been studied. In particular, the use of the taxonomies mentioned above could be a major step to overcome given limitations, since IPC and CPC represent a graph that provides available and extensive information. However, to be used in a classifier ensemble, the graph representation must be transformed into a real-valued vector. [PERSON] and [PERSON] [9] offer an outstanding solution for this challenging task with the Dissimilarity Space Embedding (DSE), which provides a dense real-valued vector of graph edit distances. Thus, we present a novel ensemble classification system based on the combination of text and taxonomic features to enhance both applicability and performance in a real-world set-up, namely the mapping of patents as an important R&D output of biomedical engineering into six fields of future innovation (e.g., telemedicine, imaging, and implants). The central question is to what extent classification performance can be improved without significantly increasing the complexity of the approach.