  In many fields, a mass of algorithms with completely different
hyperparameters have been developed to address the same type of problems.
Choosing the algorithm and hyperparameter setting correctly can promote the
overall performance greatly, but users often fail to do so due to the absence
of knowledge. How to help users to effectively and quickly select the suitable
algorithm and hyperparameter settings for the given task instance is an
important research topic nowadays, which is known as the CASH problem. In this
paper, we design the Auto-Model approach, which makes full use of known
information in the related research paper and introduces hyperparameter
optimization techniques, to solve the CASH problem effectively. Auto-Model
tremendously reduces the cost of algorithm implementations and hyperparameter
configuration space, and thus capable of dealing with the CASH problem
efficiently and easily. To demonstrate the benefit of Auto-Model, we compare it
with classical Auto-Weka approach. The experimental results show that our
proposed approach can provide superior results and achieves better performance
in a short time.
