  Tabular data is the most common data format adopted by our customers ranging
from retail, finance to E-commerce, and tabular data classification plays an
essential role to their businesses. In this paper, we present Network On
Network (NON), a practical tabular data classification model based on deep
neural network to provide accurate predictions. Various deep methods have been
proposed and promising progress has been made. However, most of them use
operations like neural network and factorization machines to fuse the
embeddings of different features directly, and linearly combine the outputs of
those operations to get the final prediction. As a result, the intra-field
information and the non-linear interactions between those operations (e.g.
neural network and factorization machines) are ignored. Intra-field information
is the information that features inside each field belong to the same field.
NON is proposed to take full advantage of intra-field information and
non-linear interactions. It consists of three components: field-wise network at
the bottom to capture the intra-field information, across field network in the
middle to choose suitable operations data-drivenly, and operation fusion
network on the top to fuse outputs of the chosen operations deeply. Extensive
experiments on six real-world datasets demonstrate NON can outperform the
state-of-the-art models significantly. Furthermore, both qualitative and
quantitative study of the features in the embedding space show NON can capture
intra-field information effectively.
