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

Class-imbalance refers to classification problems in which many moreinstances are available for certain classes than for others. Such imbalanceddatasets require special attention because traditional classifiers generallyfavor the majority class which has a large number of instances. Ensemble ofclassifiers have been reported to yield promising results. However, themajority of ensemble methods applied to imbalanced learning are static ones.Moreover, they only deal with binary imbalanced problems. Hence, this paperpresents an empirical analysis of dynamic selection techniques and datapreprocessing methods for dealing with multi-class imbalanced problems. Weconsidered five variations of preprocessing methods and fourteen dynamicselection schemes. Our experiments conducted on 26 multi-class imbalancedproblems show that the dynamic ensemble improves the AUC and the G-mean ascompared to the static ensemble. Moreover, data preprocessing plays animportant role in such cases.

ICPRAI 2018 SI: On dynamic ensemble selection and data preprocessing for multi-class imbalance learning