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

In many anomaly mining scenarios, a human expert verifies the anomaly at-the-top (as ranked by an anomaly detector) before they move on to the next. This verification produces a label—true positive (TP) or false positive (FP). In this work, we show how to leverage this label feedback for the top-1 instance to quickly re-rank the anomalies in an online fashion. In contrast to a detector that ranks once and goes offline, we propose a detector called OJRANK that works alongside the human and continues to learn (how to rank) on-the-job, i.e., from every feedback. The benefits OJRANK provides are two-fold; it reduces (i) the false positive rate by ‘muting’ the anomalies similar to FP instances; as well as (ii) the expert effort by elevating to the top the anomalies similar to a TP instance. We show that OJRANK achieves statistically significant improvement on both detection precision and human effort over the offline detector as well as existing state-of-the-art ranking strategies, while keeping the per feedback response time (to re-rank) well below a second.

Learning On-the-Job to Re-rank Anomalies from Top-1 Feedback