Abstract: Undersampling is a widely adopted method to deal with imbalance pattern classification problems. Current methods mainly depend on either random resampling on the majority class or resampling ...

In this work we focus on how to properly cross-validate when we have imbalanced data. As a matter of fact, in the context of many credit crad fraud, we have datasets where we have two classes for the ...

To avoid aliasing, we know from the Nyquist criterion that we must digitize a signal at a sampling rate of at least twice the bandwidth of the signal. For 'baseband' signals with frequency components ...

Datasets can be highly unbalanced: some values/categories may be over-represented, while others may be under-represented. Such imbalance may have a negative impact on ...

In undersampling applications, such as wideband receivers, cellular base stations, and communications receivers, the undersampled signal has a relatively low-frequency bandwidth—with the help of the ...

In compressed sensing magnetic resonance imaging (CS-MRI), undersampling of k-space is performed to achieve faster imaging. For this process, it is important to acquire data randomly, and an optimal ...

It has long been known that some listeners experience hearing difficulties out of proportion with their audiometric losses. Notably, some older adults as well as auditory neuropathy patients have ...