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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Missing value imputation is a challenging and well-researched topic in datamining. In this paper, we propose IFGAN, a missing value imputation algorithmbased on Feature-specific Generative Adversarial Networks (GAN). Our idea isintuitive yet effective: a feature-specific generator is trained to imputemissing values, while a discriminator is expected to distinguish the imputedvalues from observed ones. The proposed architecture is capable of handlingdifferent data types, data distributions, missing mechanisms, and missingrates. It also improves post-imputation analysis by preserving inter-featurecorrelations. We empirically show on several real-life datasets that IFGANoutperforms current state-of-the-art algorithm under various missingconditions.

IFGAN: Missing Value Imputation using Feature-specific Generative Adversarial Networks