Cross-project defect prediction (CPDP), where data from different software projects are used to predict defects, has been proposed as a way provide for that lack historical data. Evaluations of CPDP models using the Nearest Neighbour (NN) Filter approach have shown promising results in recent studies. A key challenge with defect-prediction datasets is class imbalance, is, highly skewed non-bugg...

Imbalanced data classification is a demanding issue in mining and machine learning. Models that learn with imbalanced input generate feeble performance the minority class. Resampling methods can handle this balance skewed dataset. Cluster-based Undersampling (CUS) Near-Miss (NM) techniques are widely used However, these suffer from some serious flaws. CUS averts impact of distance factor on ins...

We sought to evaluate the efficacy of prospective random undersampling and low-dimensional-structure self-learning and thresholding reconstruction for highly accelerated contrast-enhanced whole-heart coronary MRI. A prospective random undersampling scheme was implemented using phase ordering to minimize artifacts due to gradient switching and was compared to a randomly undersampled acquisition ...

PURPOSE Diffusion Spectrum Imaging enables to reconstruct the ensemble average propagator (EAP) at the expense of having to acquire a large number of measurements. Compressive sensing offers an efficient way to decrease the required number of measurements. The purpose of this work is to perform a thorough experimental comparison of three sampling strategies and six sparsifying transforms to sho...

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...

We study anisotropic undersampling schemes like those used in multi-dimensional NMR spectroscopy and MR imaging, which sample exhaustively in certain time dimensions and randomly in others. Our analysis shows that anisotropic undersampling schemes are equivalent to certain block-diagonal measurement systems. We develop novel exact formulas for the sparsity/undersampling tradeoffs in such measur...

We sought to evaluate the efficacy of prospective random undersampling and low-dimensional-structure self-learning and thresholding reconstruction for highly accelerated contrast-enhanced whole-heart coronary MRI. A prospective random undersampling scheme was implemented using phase ordering to minimize artifacts due to gradient switching and was compared to a randomly undersampled acquisition ...

Purpose:Diffusion Spectrum Imaging (DSI) enables to reconstruct the Ensemble Average Propagator (EAP) at the expense of having to acquire a large number of measurements. Compressive Sensing (CS) offers an efficient way to decrease the required number of measurements. The purpose of this work is to perform a thorough experimental comparison of 3 sampling strategies and 6 sparsifying transforms t...