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

The general datamining algorithm also classify the balanced dataset, when the data have imbalanced the predicted rate over minority class is still low. The random sampling techniques has been applying to solve the imbalanced data, but sometimes the random technique has selected the features is clearly different from both, when the unseen data (from minority class) has features look like the majority class, the classification model show miss classification because the model learning sample data does not complete. To improve the performance to classify the data, the genetic algorithm is applying to finding the optimal parameter, but sometimes the genetic algorithm cannot find the best set of parameters because the random initial population is not cover the best set of parameters, in this research proposed the techniques to guarantee the genetic algorithm can find the optimal parameter by using restarting technique to re-create the initial population when the new generation show powerful less than the old population. The results show that proposed technique can improve the performance to classify the minority class from imbalanced dataset more than the other techniques.

Improvement the Imbalanced Data Classification with Restarting Genetic Algorithm for Support Vector Machine Algorithm