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 Plus annual

Presents the access of premium content as premium feature

Premium Content

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 annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

We present a fast and scalable algorithm to induce non-monotonic logicprograms from statistical learning models. We reduce the problem of search forbest clauses to instances of the High-Utility Itemset Mining (HUIM) problem. Inthe HUIM problem, feature values and their importance are treated astransactions and utilities respectively. We make use of TreeExplainer, a fastand scalable implementation of the Explainable AI tool SHAP, to extract locallyimportant features and their weights from ensemble tree models. Our experimentswith UCI standard benchmarks suggest a significant improvement in terms ofclassification evaluation metrics and running time of the training algorithmcompared to ALEPH, a state-of-the-art Inductive Logic Programming (ILP) system.

Induction of Non-monotonic Logic Programs To Explain Statistical Learning Models