Support vector machine has a better classification and prediction performance on balance data classification, but has a poor performance for imbalance data. In this paper, the reason of the poor results which were produced by imbalance data is explained. And a new approach is proposed to solve the imbalance data classification. By this method the imbalance data problem is converted to several i...

abstract background: while nursing workload is a worldwide challenge, less attention has been given to the determining factors. understanding these factors is important and could help nursing managers to provide suitable working environment and to manage the adverse outcomes of nursing workload. the aim of this study was to discover nurses’ experiences of determinant factors of their workload. ...

While amblyopia involves both binocular imbalance and deficits in processing high spatial frequency information, little is known about the spatial-frequency dependence of binocular imbalance. Here we examined binocular imbalance as a function of spatial frequency in amblyopia using a novel computer-based method. Binocular imbalance at four spatial frequencies was measured with a novel dichoptic...

The critical period of respiratory development in rats is a narrow window toward the end of the second postnatal week (P12-13), when abrupt neurochemical, electrophysiological, and ventilatory changes occur, when inhibition dominates over excitation, and when the animals' response to hypoxia is the weakest. The goal of this study was to further test our hypothesis that a major mechanism underly...

The I/O subsystem is becoming a major bottleneck in an increasing number of computer systems. To provide improved I/O performance, as well as to accommodate growing storage requirements, disk subsystems are increasing in size. A major hurdle to obtaining the performance available from these large disk subsystems is load imbalance, or disk skew. Dynamic data placement, the conventional load bala...

In this study, we systematically investigate the impact of class imbalance on classification performance of convolutional neural networks (CNNs) and compare frequently used methods to address the issue. Class imbalance is a common problem that has been comprehensively studied in classical machine learning, yet very limited systematic research is available in the context of deep learning. In our...

In last few years there are major changes and evolution has been done on classification of data. As the application area of technology is increases the size of data also increases. Classification of data becomes difficult because of unbounded size and imbalance nature of data. Class imbalance problem become greatest issue in data mining. Imbalance problem occur where one of the two classes havi...

Class imbalance has been reported as an important obstacle to apply traditional learning algorithms to real-world domains. Recent investigations have questioned whether the imbalance is the unique factor that hinders the performance of classifiers. In this paper, we study the behavior of six algorithms when classifying imbalanced, overlapped data sets under uncommon situations (e.g., when the o...