  Interpretability has become an essential topic for artificial intelligence in
some high-risk domains such as healthcare, bank and security. For commonly-used
tabular data, traditional methods trained end-to-end machine learning models
with numerical and categorical data only, and did not leverage human
understandable knowledge such as data descriptions. Yet mining human-level
knowledge from tabular data and using it for prediction remain a challenge.
Therefore, we propose a concept and argumentation based model (CAM) that
includes the following two components: a novel concept mining method to obtain
human understandable concepts and their relations from both descriptions of
features and the underlying data, and a quantitative argumentation-based method
to do knowledge representation and reasoning. As a result of it, CAM provides
decisions that are based on human-level knowledge and the reasoning process is
intrinsically interpretable. Finally, to visualize the purposed interpretable
model, we provide a dialogical explanation that contain dominated reasoning
path within CAM. Experimental results on both open source benchmark dataset and
real-word business dataset show that (1) CAM is transparent and interpretable,
and the knowledge inside the CAM is coherent with human understanding; (2) Our
interpretable approach can reach competitive results comparing with other
state-of-art models.
