DeepAI AI Chat
Log In Sign Up

Constrained Generalized Additive 2 Model with Consideration of High-Order Interactions

by   Akihisa Watanabe, et al.

In recent years, machine learning and AI have been introduced in many industrial fields. In fields such as finance, medicine, and autonomous driving, where the inference results of a model may have serious consequences, high interpretability as well as prediction accuracy is required. In this study, we propose CGA2M+, which is based on the Generalized Additive 2 Model (GA2M) and differs from it in two major ways. The first is the introduction of monotonicity. Imposing monotonicity on some functions based on an analyst's knowledge is expected to improve not only interpretability but also generalization performance. The second is the introduction of a higher-order term: given that GA2M considers only second-order interactions, we aim to balance interpretability and prediction accuracy by introducing a higher-order term that can capture higher-order interactions. In this way, we can improve prediction performance without compromising interpretability by applying learning innovation. Numerical experiments showed that the proposed model has high predictive performance and interpretability. Furthermore, we confirmed that generalization performance is improved by introducing monotonicity.


page 1

page 2

page 3

page 4


Higher-order Neural Additive Models: An Interpretable Machine Learning Model with Feature Interactions

Black-box models, such as deep neural networks, exhibit superior predict...

GAMI-Net: An Explainable Neural Network based on Generalized Additive Models with Structured Interactions

The lack of interpretability is an inevitable problem when using neural ...

Higher-order generalized-α methods for parabolic problems

We propose a new class of high-order time-marching schemes with dissipat...

Adaptive Explainable Neural Networks (AxNNs)

While machine learning techniques have been successfully applied in seve...

Additive Higher-Order Factorization Machines

In the age of big data and interpretable machine learning, approaches ne...

Higher-order generalized-α methods for hyperbolic problems

The generalized-α time-marching method provides second-order accuracy in...

Data-driven discovery of non-Newtonian astronomy via learning non-Euclidean Hamiltonian

Incorporating the Hamiltonian structure of physical dynamics into deep l...