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/

Zendy Plus

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

Zendy Tools

Zendy Open

Feature engineering has become one of the most important steps to improvemodel prediction performance, and to produce quality datasets. However, thisprocess requires non-trivial domain-knowledge which involves a time-consumingprocess. Thereby, automating such process has become an active area of researchand of interest in industrial applications. In this paper, a novel method,called Meta-learning and Causality Based Feature Engineering (MACFE), isproposed; our method is based on the use of meta-learning, feature distributionencoding, and causality feature selection. In MACFE, meta-learning is used tofind the best transformations, then the search is accelerated by pre-selecting"original" features given their causal relevance. Experimental evaluations onpopular classification datasets show that MACFE can improve the predictionperformance across eight classifiers, outperforms the current state-of-the-artmethods in average by at least 6.54%, and obtains an improvement of 2.71% overthe best previous works.

MACFE: A Meta-learning and Causality Based Feature Engineering Framework