  Examining the effect of different encoding techniques on entity and context
embeddings, the goal of this work is to challenge commonly used Ordinal
encoding for tabular learning. Applying different preprocessing methods and
network architectures over several datasets resulted in a benchmark on how the
encoders influence the learning outcome of the networks. By keeping the test,
validation and training data consistent, results have shown that ordinal
encoding is not the most suited encoder for categorical data in terms of
preprocessing the data and thereafter, classifying the target variable
correctly. A better outcome was achieved, encoding the features based on string
similarities by computing a similarity matrix as input for the network. This is
the case for both, entity and context embeddings, where the transformer
architecture showed improved performance for Ordinal and Similarity encoding
with regard to multi-label classification tasks.
