In many real world settings, imbalanced data impedes model performance of learning
algorithms, like neural networks, mostly for rare cases. This is especially problematic for …

The problem of imbalanced domains, framed within predictive tasks, is relevant in many
practical applications. When dealing with imbalanced domains a performance degradation …

Power outages caused by extreme weather events cost the economy of the United States
billions of dollars every year and endanger the lives of the people affected by them. These …

Planted forests are critical to climate change mitigation and constitute a major supplier of
timber/non-timber products and other ecosystem services. Globally, approximately 36% of …

Continuous integration testing is an important step in the modern software engineering life
cycle. Test prioritization is a method that can improve the efficiency of continuous integration …

Plant diseases are responsible for global economic losses due to degradation in the quality
and productivity of plants. Therefore, plant disease prediction has become an essential area …

This work presents a computer-aided framework for allergy diagnosis which is capable of
handling comorbidities. The system was developed using datasets collected from allergy …

Imbalanced domains are an important problem frequently arising in real world predictive
analytics. A significant body of research has addressed imbalanced distributions in …

Imbalanced problems can arise in different real-world situations, and to address this, certain
strategies in the form of resampling or balancing algorithms are proposed. This issue has …

In our age of big data and growing computational power, versatility in data analysis is
important. This study presents a flexible way to combine statistics and machine learning for …