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

When the training data in a two-class classification problem is overwhelmedby one class, most classification techniques fail to correctly identify thedata points belonging to the underrepresented class. We proposeSimilarity-based Imbalanced Classification (SBIC) that learns patterns in thetraining data based on an empirical similarity function. To take the imbalancedstructure of the training data into account, SBIC utilizes the concept ofabsent data, i.e. data from the minority class which can help better find theboundary between the two classes. SBIC simultaneously optimizes the weights ofthe empirical similarity function and finds the locations of absent datapoints. As such, SBIC uses an embedded mechanism for synthetic data generationwhich does not modify the training dataset, but alters the algorithm to suitimbalanced datasets. Therefore, SBIC uses the ideas of both major schools ofthoughts in imbalanced classification: Like cost-sensitive approaches SBICoperates on an algorithm level to handle imbalanced structures; and similar tosynthetic data generation approaches, it utilizes the properties of unobserveddata points from the minority class. The application of SBIC to imbalanceddatasets suggests it is comparable to, and in some cases outperforms, othercommonly used classification techniques for imbalanced datasets.

Empirical Similarity for Absent Data Generation in Imbalanced Classification