<figure xmlns="http://www.tei-c.org/ns/1.0"><graphic url="192_final-d93fbd15a243ba286b7585631af91792.png" /><head>Figure 1: Novels per Genre</head></figure><lb xmlns="http://www.tei-c.org/ns/1.0" /><anchor xmlns="http://www.tei-c.org/ns/1.0" xml:id="id_docs-internal-guid-2a5365f3-7fff-66ec-c472-4e67ab9c5e5a" /><hi xmlns="http://www.tei-c.org/ns/1.0" rend="color(#000000)">We have employed three groups of methods: traditional feature-based classifiers (Group A), modern feature-based classifiers (Group B) and deep learning (Group C). While Group A and B are based on document-term-matrix (20,000 most-frequent-words, tf-idf-weighted, stopwords removed, dimensionality reduction with LSI to 1000 features) as input, Group C works with unprocessed text. Named entities are removed completely. Hyperparameter optimization was done by sampling from the space of values recommended by the documentation of the libraries and (Olson et al. 2017) using Optuna (Akiba et al. 2019): In table 1 we report the best performance. We evaluated the performance of the deep learning approaches in advance on a smaller dataset, so that later only the best architecture had to be extensively tested (table 1). To increase speed initialized with pre-trained (wikipedia.de+30.000 novels) fasttext embeddings (Bojanowski et al. 2016). As a compromise between performance and speed we used the BiRNN architecture for all following experiments. </hi>