How to improve the interpretability of kernel learning
This work addresses the need for more interpretable machine learning models, particularly in kernel methods, but it is incremental as it builds on existing supervised learning frameworks.
The paper tackles the trade-off between interpretability and generalization in kernel learning by proposing a quantitative interpretability index and a universal learning framework to balance both, achieving a theoretical probability bound and applying it to least-squares support vector machines with experimental validation.
In recent years, machine learning researchers have focused on methods to construct flexible and interpretable prediction models. However, an interpretability evaluation, a relationship between generalization performance and an interpretability of the model and a method for improving the interpretability have to be considered. In this paper, a quantitative index of the interpretability is proposed and its rationality is proved, and equilibrium problem between the interpretability and the generalization performance is analyzed. Probability upper bound of the sum of the two performances is analyzed. For traditional supervised kernel machine learning problem, a universal learning framework is put forward to solve the equilibrium problem between the two performances. The condition for global optimal solution based on the framework is deduced. The learning framework is applied to the least-squares support vector machine and is evaluated by some experiments.