According to the World Health Organization (WHO), it has been recorded that up now more than 150 million people have diabetes, whether they are elderly people, adults, teenagers, men or women. Early knowledge of diabetes can be seen based on data from patients who already diabetes. The patient's disease previously stored and arranged in a warehouse what is commonly referred as dataset. Therefor...

In a concept learning problem, imbalances in the distribution of the data can occur either between the two classes or within a single class. Yet, although both types of imbalances are known to affect negatively the performance of standard classifiers, methods for dealing with the class imbalance problem usually focus on rectifying the between-class imbalance problem, neglecting to address the i...

This paper asks at what level of class imbalance one-class classifiers outperform two-class classifiers in credit scoring problems in which class imbalance, referred to as the low-default portfolio problem, is a serious issue. The question is answered by comparing the performance of a variety of one-class and two-class classifiers on a selection of credit scoring datasets as the class imbalance...

This study examines whether the direction and magnitude of the aggregate order-imbalance of the index stocks can explain the arbitrage spread between index futures and the underlying cash index. The data are for the Asian financial crisis period and hence entail wide variations in order imbalance and the index-futures basis. The analysis controls for realistic trading costs and actual dividend ...

For this article, we explore a hypothesis involving the possible role of reduction/oxidation (redox) state in cancer. We hypothesize that many modifications in cellular macromolecules, observed in cancer progression, may be caused by redox imbalance. Recent biochemical data suggest that human prostate cancer cell lines show a redox imbalance (oxidizing) compared with benign primary prostate epi...

Conventional classification algorithms do not provide accurate results when the data distribution (class sizes) is unequal or corrupted with noise because are biased towards bigger class. In many real life cases, there a requirement to uncover unusual/smaller classes. There bundle of examples where importance smaller/rare class much-much higher than for example- brain tumor detection, credit ca...

The class imbalance problems have been reported to severely hinder classification performance of many standard learning algorithms, and have attracted a great deal of attention from researchers of different fields. Therefore, a number of methods, such as sampling methods, cost-sensitive learning methods, and bagging and boosting based ensemble methods, have been proposed to solve these problems...

The model is an abstraction of the reality. The selection of the usual inverse binomial as an underlying model for the number of patients waiting in months for heart and lung transplant is questionable because the data exhibit not the required balance between the dispersion and its functional equivalent in terms of the mean but rather an over or under dispersion. This phenomenon of over/under d...