Abstract Class imbalance occurs when the class distribution is not equal. Namely, one under-represented (minority class), and other has significantly more samples in data (majority class). The problem prevalent many real world applications. Generally, minority of interest. synthetic over-sampling technique (SMOTE) method considered most prominent for handling unbalanced data. SMOTE generates ne...

Nyquist rate converters have traditionally been used for low to moderate resolution data acquisition applications. In this paper, we describe a feedforward residue compensation (FRC) technique to extend the resolution of such converters using an oversampling modulator loop. Closed-form analytical expressions are developed to provide insight into the operation and limits on the resolution of suc...

Let Ψ = {ψ1, . . . , ψL} ⊂ L2 := L2(−∞,∞) generate a tight affine frame with dilation factor M , where 2 ≤M ∈ Z, and sampling constant b = 1 (for the zeroth scale level). Then for 1 ≤ N ∈ Z, N×oversampling (or oversampling by N) means replacing the sampling constant 1 by 1/N . The Second Oversampling Theorem asserts that N×oversampling of the given tight affine frame generated by Ψ preserves a ...

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 Lth-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 ove...

This document describes the theory of oversampling, the hardware and software implementation on the TMS320C240 and important aspects that need to be considered when using oversampling. The different techniques with their main advantages, limitations and applications are covered in chapter 2. Chapter 3 describes the key implementation blocks that are required to implement oversampling on the TMS...

Abstract. The properties of oversampled affine frames are considered here with two main goals in mind. The first goal is to generalize the approach of Chui and Shi to the matrix oversampling setting for expanding, lattice-preserving dilations, whereby we obtain a new proof of the Second Oversampling Theorem for affine frames. The Second Oversampling Theorem, proven originally by Ron and Shen vi...

This study presents a novel hybrid framework combining feature selection, oversampling, and machine learning (ML) to improve the prediction performance of vehicle insurance. The addresses class imbalance problem in binary classification tasks by employing principal component analysis for synthetic minority oversampling technique random forest ML classifier prediction. results demonstrate that p...

It is well known that classifiers trained using imbalanced datasets usually have a bias toward the majority class. In this context, classification models can present high performance overall and for class, even when minority class significantly lower. This paper presents genetic programming (GP) model with crossover-based oversampling technique dataset binary text classification. The aim of stu...