Auto-Model: Utilizing Research Papers and HPO Techniques to Deal with the CASH problem

TL;DR

Auto-Model leverages research papers and hyperparameter optimization to efficiently solve the CASH problem, improving algorithm selection and configuration with reduced cost and better performance.

Contribution

It introduces Auto-Model, a novel approach that combines research paper data and HPO techniques to address the CASH problem effectively.

Findings

Auto-Model outperforms classical Auto-Weka in experiments.

Auto-Model reduces algorithm implementation and hyperparameter tuning costs.

Auto-Model achieves superior performance in a shorter time.

Abstract

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.