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

Training of Machine Learning (ML) models in real contexts often deals withbig data sets and high-class imbalance samples where the class of interest isunrepresented (minority class). Practical solutions using classical ML modelsaddress the problem of large data sets using parallel/distributedimplementations of training algorithms, approximate model-based solutions, orapplying instance selection (IS) algorithms to eliminate redundant information.However, the combined problem of big and high imbalanced datasets has been lessaddressed. This work proposes three new methods for IS to be able to deal withlarge and imbalanced data sets. The proposed methods use Locality SensitiveHashing (LSH) as a base clustering technique, and then three different samplingmethods are applied on top of the clusters (or buckets) generated by LSH. Thealgorithms were developed in the Apache Spark framework, guaranteeing theirscalability. The experiments carried out in three different datasets suggestthat the proposed IS methods can improve the performance of a base ML modelbetween 5% and 19% in terms of the geometric mean.

An Instance Selection Algorithm for Big Data in High imbalanced datasets based on LSH