Exploiting Expertise Rules for Statistical Data-Driven Modeling

摘要

A variety of real-world applications such as complex industry process usually are lack of abundant training samples since the data acquiring process is time and labor consuming. Hence, it is important to utilize the limited training samples to build a sophisticated data-driven model, which may improve industry productivity. Recently, nonlinear learning models such as artificial neural networks and support vector machines have shown to be effective in modeling small-scale data by their strong modeling ability. However, these nonlinear learning models work as a black box and are often not human understandable and are difficult to be interpreted. In addition, in many applications, domain experts could provide us valuable expertise knowledge which may help further improve the modeling process. In this paper, we propose to integrate expertise knowledge to the nonlinear learning model to advance the data-driven modeling process in real-world applications. Experimental results on six benchmark datasets and a real-world industry application validate the effectiveness of the proposed model.