English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

In recent years, machine learning and AI have been introduced in manyindustrial fields. In fields such as finance, medicine, and autonomous driving,where the inference results of a model may have serious consequences, highinterpretability as well as prediction accuracy is required. In this study, wepropose CGA2M+, which is based on the Generalized Additive 2 Model (GA2M) anddiffers from it in two major ways. The first is the introduction ofmonotonicity. Imposing monotonicity on some functions based on an analyst'sknowledge is expected to improve not only interpretability but alsogeneralization performance. The second is the introduction of a higher-orderterm: given that GA2M considers only second-order interactions, we aim tobalance interpretability and prediction accuracy by introducing a higher-orderterm that can capture higher-order interactions. In this way, we can improveprediction performance without compromising interpretability by applyinglearning innovation. Numerical experiments showed that the proposed model hashigh predictive performance and interpretability. Furthermore, we confirmedthat generalization performance is improved by introducing monotonicity.

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