Synthetic Data Generator for Classification Rules Learning

Liu, Runzong; Fang, Bin; Tang, Yuan Yan; Chan, Patrick P. K.

doi:10.1109/ccbd.2016.076

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…Liu [14] have created a synthetic data generator for assessing learning rules classification. The work generates learning rules based on the attributes entered by the user to build relationships between these attributes, and the technique used for the data was decision tree algorithms.…”

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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“…using a small sample of available data and replicating it while adding a certain amount of noise [20], [21] 2. using available dataset as input to an (un)supervised machine learning algorithm such as a neural network, decision tree, Hidden Markov Model, etc. which will learn hidden properties of data and provide expanded dataset as an output [17], [19], [22] 3. generating a new dataset based on some fundamental properties of a real-life dataset, without the need to have access to actual real-life data [16], [18], [23]. Generating synthetic social graphs has certain special features compared to generating ''ordinary'' synthetic datasets.…”

Section: Synthetic Datasets Generationmentioning

confidence: 99%

Analysis of Facebook Interaction as Basis for Synthetic Expanded Social Graph Generation

2019

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“…Kwon et al [9] used drawing interactions to direct the visualization of high-dimensional data according to the users' domain knowledge. Liu [10] created a synthetic data generator to evaluate the learning of classification rules. Similarly, researchers have proposed database synthesizers to analyze data mining tools [11]- [13].…”

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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Synthetic Data Generator for Classification Rules Learning

Cited by 6 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

Analysis of Facebook Interaction as Basis for Synthetic Expanded Social Graph Generation

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

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