We focus on modelling categorical features
and improving predictive power of neural networks with mixed
categorical and numerical features in supervised learning tasks.
The goal of the paper is to challenge the current dominant approach
in actuarial data science with a new architecture of a neural
network and a new training algorithm. The key proposal is to use a
joint embedding for all categorical features, instead of separate
entity embeddings, to determine the numerical representation of the
categorical features which is fed, together with all other
numerical features, into hidden layers of a neural network with a
target response.
In addition, we postulate that we should initialize the numerical
representation of the categorical features and other parameters of
the hidden layers of the neural network with parameters trained
with (denoising) autoencoders in unsupervised learning tasks,
instead of using random initialization of parameters. Since
autoencoders for categorical data play an important role in this
research, they are investigated in more depth in the paper. We
illustrate our ideas with experiments on a real data set with claim
numbers, and we demonstrate that we can achieve a higher predictive
power of the network.
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