Knowledge-based Residual Learning

Small data has been a barrier for many machine learning tasks, especially when applied in scientific domains. Fortunately, we can utilize domain knowledge to make up the lack of data. Hence, in this paper, we propose a hybrid model KRL that treats domain knowledge model as a weak learner and uses another neural net model to boost it. We prove that KRL is guaranteed to improve over pure domain knowledge model and pure neural net model under certain loss functions. Extensive experiments have shown the superior performance of KRL over baselines. In addition, several case studies have explained how the domain knowledge can assist the prediction.

Authors

• Guanjie Zheng
• Chang Liu Tsinghua University
• Hua Wei Arizona State University
• Porter Jenkins Brigham Young University
• Chacha Chen
• Tao Wen
• Zhenhui Li University of Electronic Science and Technology of China

Keywords

• Data Mining: Classification
• Data Mining: Mining Spatial, Temporal Data
• Data Mining: Theoretical Foundation of Data Mining