Sampling methods are a direct approach to tackle the problem of class imbalance. These methods sample a data set in order to alter the class distributions. Usually these methods are applied to obtain a more balanced distribution. An open-ended question about sampling methods is which distribution can provide the best results, if any. In this work we develop a broad empirical study aiming to pro...

Evaluating classifier performance with ROC curves is popular in the machine learning community. To date, the only method to assess confidence of ROC curves is to construct ROC bands. In the case of severe class imbalance with few instances of the minority class, ROC bands become unreliable. We propose a generic framework for classifier evaluation to identify a segment of an ROC curve in which m...

Nosocomial infections (NIs)---those acquired in health care settings---are among the major causes of increased mortality among hospitalized patients. They are a significant burden for patients and health authorities alike; it is thus important to monitor and detect them through an effective surveillance system. This paper describes a retrospective analysis of a prevalence survey of NIs done in ...

Class imbalance occurs when the distribution of classes between majority and minority is not same. The data on imbalanced may vary from mild to severe. effect high-class affect overall classification accuracy since model most likely predict that fall within class. Such a will give biased results, performance predictions for class often have no impact model. use oversampling technique one way de...

In this paper we propose, develop, and test a new single-feature evaluator called Significant Proportion of Target Instances (SPTI) to handle the direct-marketing data with the class imbalance problem. The SPTI feature evaluator demonstrates its stability and outstanding performance through empirical experiments in which the real-world customer data of an e-recruitment firm are used. This resea...

Class imbalance is a prevalent problem that not only reduces the performance of machine learning techniques but also causes lacking inherent complex characteristics data. Though researchers have proposed various ways to deal with problem, they yet consider how select proper treatment, especially when uncertainty levels are high. Applying rough-fuzzy theory imbalanced data could be promising res...