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 this paper we present a study on the behaviour of some representative Bayesian Networks Classifiers  (BNCs), when the dataset they are learned from presents imbalanced data, that is, there are far fewer cases  labelled with a particular class value than with the other ones (assuming binary classification problems). This  is a typical source of trouble in some datasets, and the development of more robust techniques is currently  very important. In this study, we have selected a benchmark of 129 imbalanced datasets, and performed an  analytical approach focusing on BNCs. Our results show good performance of these classifiers, that outperform  decision trees (C4.5). Finally, an algorithm to improve the performance of any BNC is also given. We  have carried out an experimentation where we show how the using of oversampling of the minority class to  achieve the desired value for the imbalance ratio (IR), which is the division of the number of cases for the  majority class by the cases of the minority class. From this work we can conclude that BNCs show a very  good performance for imbalanced datasets, and that our proposal enhance their results for those datasets that  provided poor results.

Impact on Bayesian Networks Classifiers When Learning from Imbalanced Datasets