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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

For several years till date, the major issues in terms of solving forclassification problems are the issues of Imbalanced data. Because majority ofthe machine learning algorithms by default assumes all data are balanced, thealgorithms do not take into consideration the distribution of the data sampleclass. The results tend to be unsatisfactory and skewed towards the majoritysample class distribution. This implies that the consequences as a result ofusing a model built using an Imbalanced data without handling for the Imbalancein the data could be misleading both in practice and theory. Most researchershave focused on the application of Synthetic Minority Oversampling Technique(SMOTE) and Adaptive Synthetic (ADASYN) Sampling Approach in handling dataImbalance independently in their works and have failed to better explain thealgorithms behind these techniques with computed examples. This paper focuseson both synthetic oversampling techniques and manually computes synthetic datapoints to enhance easy comprehension of the algorithms. We analyze theapplication of these synthetic oversampling techniques on binary classificationproblems with different Imbalanced ratios and sample sizes.

Handling Imbalanced Data: A Case Study for Binary Class Problems