While it is tempting in experimental practice to seek as high a data rate possible, oversampling can become an issue if one takes measurements too densely. These effects take many forms, some of which are easy detect: e.g., when the sequence contains multiple copies same measured value. In other situations, there mixing$-$in measurement apparatus and/or system itself$-$oversampling be harder de...

Standard classification algorithms often face a challenge of learning from imbalanced datasets. While several approaches have been employed in addressing this problem, methods that involve oversampling minority samples remain more widely used comparison to algorithmic modifications. Most variants are derived Synthetic Minority Oversampling Technique (SMOTE), which involves generation synthetic ...

For class imbalance problem, the integration of sampling and ensemble methods has shown great success among various methods. Nevertheless, as the representatives of sampling methods, undersampling and oversampling cannot outperform each other. That is, undersampling fits some data sets while oversampling fits some other. Besides, the sampling rate also significantly influences the performance o...

In this paper, design methods for perfect reconstruction (PR) oversampled cosine-modulated filter banks with integer oversampling factors and arbitrary delay are presented. The system delay, which is an important parameter in realtime applications, can be chosen independently of the prototype lengths. Oversampling gives us additional freedom in the filter design process, which can be exploited ...

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...

Faster observation of severe weather is a primary need of radar users. However, modifying scanning strategies to provide faster updates usually leads to trade-offs such as losses in data quality and/or spatial resolution. Range oversampling techniques can lead to faster updates and/or lower estimation errors without increasing the transmit bandwidth and with minimal degradation of the spatial r...

Classification problem for imbalanced datasets is pervasive in a lot of data mining domains. Imbalanced classification has been a hot topic in the academic community. From data level to algorithm level, a lot of solutions have been proposed to tackle the problems resulted from imbalanced datasets. SMOTE is the most popular data-level method and a lot of derivations based on it are developed to ...