Incorporating prior domain knowledge into inductive machine learning
Ting Jung Yu, Tony Jan, Simeon J. Simoff, John James Debenham · 2007
The paper reviews the recent developments of incorporating prior domain knowledge into inductive machine learning, and proposes a guideline that incorporates prior domain knowledge in three key issues of inductive machine learning algorithms: consistency, generalization and convergence. With respect to each issue, this paper gives some approaches to improve the performance of the inductive machine learning algorithms and discusses the risks of incorporating domain knowledge. As a case study, a hierarchical modelling method, VQSVM, is proposed and tested over some imbalanced data sets with various imbalance ratios and various numbers of subclasses.