We find necessary and sufficient conditions for (shifted) oversampling expansions to hold in wavelet subspaces. In particular, we characterize scaling functions with the (shifted) oversampling property. We also obtain L and L∞ norm estimates for the truncation and aliasing errors of the oversampling expansion.

Intrusion detection of IoT-based data is a hot topic and has received lot interests from researchers practitioners since the security IoT networks crucial. Both supervised unsupervised learning methods are used for intrusion networks. This paper proposes an approach three stages considering clustering with reduction stage, oversampling classification by Single Hidden Layer Feed-Forward Neural N...

We developed a multiscale object-based classification method for detecting diseased trees (Japanese Oak Wilt and Japanese Pine Wilt) in high-resolution multispectral satellite imagery. The proposed method involved (1) a hybrid Intensity-Hue-Saturation (IHS)/Smoothing Filter-based Intensity Modulation (SFIM) pansharpening approach 10 (IHS-SFIM) to obtain more spatially and spectrally accurate im...

One way to handle data mining problems where class prior probabilities and/or misclassification costs between classes are highly unequal is to resample the data until a new, desired class distribution in the training data is achieved. Many resampling techniques have been proposed in the past, and the relationship between resampling and cost-sensitive learning has been well studied. Surprisingly...

Abstract Machine learning plays an increasingly significant role in the building of Network Intrusion Detection Systems. However, machine models trained with imbalanced cybersecurity data cannot recognize minority data, hence attacks, effectively. One way to address this issue is use resampling, which adjusts ratio between different classes, making more balanced. This research looks at resampli...

Imbalanced class distribution is a challenging problem in many real-life classification problems. Existing synthetic oversampling do suffer from the curse of dimensionality because they rely heavily on Euclidean distance. This paper proposed a new method, called Minority Oversampling Technique based on Local Densities in Low-Dimensional Space (or MOT2LD in short). MOT2LD first maps each trainin...

Oversampling is a common characteristic of data representing dynamic networks. It introduces noise into representations of dynamic networks, but there has been little work so far to compensate for it. Oversampling can affect the quality of many important algorithmic problems on dynamic networks, including link prediction. Link prediction seeks to predict edges that will be added to the network ...

This paper describes an analog-to-digital converter which combines multiple delta-sigma modulators in parallel so that time oversampling may be reduced or even eliminated. By doubling the number of L th-order delta-sigma modulators, the resolution of this architecture is increased by approximately L bits. Thus, the resolution obtained by combining M delta-sigma modulators in parallel with no ov...

Fault detection prediction of FAB (wafer fabrication) process in semiconductor manufacturing process is possible that improve product quality and reliability in accordance with the classification performance. However, FAB process is sometimes due to a fault occurs. And mostly it occurs “pass”. Hence, data imbalance occurs in the pass/fail class. If the data imbalance occurs, prediction models a...