A prototype of synthetic data generator

García, Diego; Millán, Marta

doi:10.1109/colomcc.2011.5936311

Cited by 3 publications

(2 citation statements)

References 10 publications

(8 reference statements)

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“…García and Millán [19] have created a system to generate synthetic data that can be used by a wide range of scientific areas. The authors compared their application with the tools that already exist in the market and have shown the pros and cons of their generator system.…”

Section: Related Workmentioning

confidence: 99%

Synthetic Datasets Generator for Testing Information Visualization and Machine Learning Techniques and Tools

et al. 2020

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Data generators are applications that produce synthetic datasets, which are useful for testing data analytics applications, such as machine learning algorithms and information visualization techniques. Each data generator application has a different approach to generate data. Consequently, each one has functionality gaps that make it unsuitable for some tasks (e.g., lack of ways to create outliers and non-random noise). This paper presents a data generator application that aims to fill relevant gaps scattered across other applications, providing a flexible tool to assist researchers in exhaustively testing their techniques in more diverse ways. The proposed system allows users to define and compose known statistical distributions to produce the desired outcome, visualizing the behavior of the data in real-time to analyze if it has the characteristics needed for efficient testing. This paper presents in detail the tool functionalities and how to create datasets, as well as a usage scenario to illustrate the process of data creation. INDEX TERMS Synthetic dataset generator, benchmark datasets creation, data creation system.

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Section: Related Workmentioning

confidence: 99%

Synthetic Datasets Generator for Testing Information Visualization and Machine Learning Techniques and Tools

et al. 2020

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“…However, these generators are specific to a problem tool or context, limiting large-scale use in other areas. Other works created a free dataset generation system for a wide range of areas as an alternative to systems from market applications [14].…”

Section: Introductionmentioning

confidence: 99%

Generation of a Synthetic Dataset for the Study of Fraud through Deep Learning Techniques

Sánchez¹,

Olmedo²,

Narvaez³

et al. 2021

International Journal on Advanced Science, Engineering and Information Technology

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Fraud is defined as any purposeful or deliberate act including cunning, deception, or other unfair means to deprive someone of property or money. Nowadays, fraud-related activities are growing at a dizzying rate, causing substantial economic losses every year. For an adequate analysis of this phenomenon, it is necessary to have data that evidences this behavior. Even so, given that these data are scarce and difficult to find, generating synthetic data for their study is a viable option. We designed two algorithms to generate text to create a synthetic data set that allows fraud analysis. These algorithms rely on the Fraud Triangle Theory proposed by Donald R. Cressey and use Recurrent Neural Network (RNN) and Long Short-Term Memory Networks (LSTM), respectively. The datasets generated were analyzed from the semantic point of view, giving a score about their readability and grammar consistency. The results obtained from this evaluation indicate that the data generation architecture proposed using the LSTM algorithm provides better performance in sentence readability (efficiency greater than 70%) than RNN (less than 40%). With LSTM, it was possible to synthesize a comprehensive data set related to the fraud triangle's vertices. This will make it easier to investigate fraudulent actions that are linked to human behavior. We will present a fraud predictor system based on machine learning techniques in the future.

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A Prototype Application to Generate Synthetic Datasets for Information Visualization Evaluations

Briro

Santos

Mendonça

et al. 2018

2018 22nd International Conference Information Visualisation (IV)

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A prototype of synthetic data generator

Cited by 3 publications

References 10 publications

Synthetic Datasets Generator for Testing Information Visualization and Machine Learning Techniques and Tools

Synthetic Datasets Generator for Testing Information Visualization and Machine Learning Techniques and Tools

Generation of a Synthetic Dataset for the Study of Fraud through Deep Learning Techniques

A Prototype Application to Generate Synthetic Datasets for Information Visualization Evaluations

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