In this paper, we present a new discrete undersampling scheme designed to favor wavefield reconstruction by sparsity-promoting inversion with transform elements that are localized in the Fourier domain. Our work is motivated by empirical observations in the seismic community, corroborated by recent results from compressive sampling, which indicate favorable (wavefield) reconstructions from rand...

Chemical Named Entity Recognition (NER) is the basic step for consequent information extraction tasks such as named entity resolution, drug-drug interaction discovery, extraction of the names of the molecules and their properties. Improvement in the performance of such systems may affects the quality of the subsequent tasks. Chemical text from which data for named entity recognition is extracte...

Abstract Class imbalance is an important consideration for cybersecurity and machine learning. We explore classification performance in detecting web attacks the recent CSE-CIC-IDS2018 dataset. This study considers a total of eight random undersampling (RUS) ratios: no sampling, 999:1, 99:1, 95:5, 9:1, 3:1, 65:35, 1:1. Additionally, seven different classifiers are employed: Decision Tree (DT), ...

We propose a new mechanism for undersampling chaotic numbers obtained by the ring coupling of one-dimensional maps. In the case of 2 coupled maps this mechanism allows the building of a PRNG which passes all NIST Test. This new geometric undersampling is very effective for generating 2 parallel streams of pseudorandom numbers, as we show, computing carefully their properties, up to sequences of...

This paper presents the performance of a classifier built using the stackingC algorithm in nine different data sets. Each data set is generated using a sampling technique applied on the original imbalanced data set. Five new sampling techniques are proposed in this paper (i.e., SMOTERandRep, Lax Random Oversampling, Lax Random Undersampling, Combined-Lax Random Oversampling Undersampling, and C...

Fig. 2: In-vivo results for RCA images: a) Reference image, b) Image from simulated prospective undersampling (R=1, i.e. data is fully sampled) with random profile ordering, c) Reconstructed image from true prospective undersampling (R=2) with proposed profile ordering. Minimization of Imaging Artifacts from Profile Ordering of Randomly Selected ky-kz Lines for Prospective CompressedSensing Acq...

Class imbalance is a frequent problem found in bioinformatics datasets. Unfortunately, the minority class is usually also the class of interest. One of the methods to improve this situation is data sampling. There are a number of different data sampling methods, each with their own strengths and weaknesses, which makes choosing one a difficult prospect. In our work we compare three data samplin...

Abstract Output thresholding is well-suited for addressing class imbalance, since the technique does not increase dataset size, run risk of discarding important instances, or modify an existing learner. Through use Credit Card Fraud Detection Dataset, this study proposes a threshold optimization approach that factors in constraint True Positive Rate (TPR) ≥ Negative (TNR). Our findings indicate...

Swarm intelligence mimics the behaviours of social insects like bees, wasps and ants to offer powerful problem solving metaheuristic which lies in a network of interactions amongst the agents of a multiagent system as well as with their environment. One of the computer algorithms inspired by swarm intelligence is the stochastic diffusion search (SDS). SDS uses some of the processes and techniqu...

Because the numbers of cars reflect each person's travel behaviors for specific location, car ownership demand model plays a dominant role in analysis order to understand area's individual and household behaviors. However, study project master plan Khon Kaen expressway represented imbalanced data; namely, majority class minority were not equal. Before developing machine learning model, this sug...