Predicting student attrition is an intriguing yet challenging problem for any academic
institution. Class-imbalanced data is a common in the field of student retention, mainly …

In medical data sets, data are predominately composed of “normal” samples with only a
small percentage of “abnormal” ones, leading to the so-called class imbalance problems. In …

The purpose of an early warning system (EWS) is to issue warning signals prior to extreme
events. Extreme weather events, however, are hard to predict due to their chaotic behavior …

In many real application areas, the data used are highly skewed and the number of
instances for some classes are much higher than that of the other classes. Solving a …

Handwriting text recognition for computer systems has been the subject of more and more
research. Recognition of Arabic handwritten text is always an ongoing challenge, mainly …

Diabetes is a complex disease that is increasing in prevalence around the world. Type 2
diabetes mellitus (T2DM) accounts for about 90–95% of all diagnosed adult cases of …

The potential arch crown settlement is one of the most hazardous factors in shallow-buried
tunnel excavations. Therefore, accurate prediction of arch crown settlement range is …

RapidMiner, R and Weka were used to study the various methods for overcoming the class
imbalance problem. As outlined in the previous chapter there are two approaches to dealing …

Liver disease is one of the leading causes of mortality in India, as it is in rest of the world.
This paper presents a survey on intelligent techniques applied to liver disorders between the …

The improved fuzzy c-means (IFCM) algorithm is an effective technique for handling the
“uniform effect” in imbalanced data clustering; it adjusts the weight of each class based on …