DEGAIN: Generative-Adversarial-Network-Based Missing Data Imputation

Shahbazian, Reza; Trubitsyna, Irina

doi:10.3390/info13120575

Cited by 13 publications

(4 citation statements)

References 38 publications

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“…In their paper (Wang et al, 2021), the authors used pseudo-label conditional generative adversarial imputation networks (PC-GAIN) to deal with incomplete data problems. Shahbazian and Trubitsyna in their study (Shahbazian & Trubitsyna, 2022) proposed a novel method for handling missing data -DEGAIN -which is an improved version of generative adversarial imputation networks (GAIN) proposed by Yoon, Jordon and Van Der Schaar (Yoon et al, 2018). Compared to GAIN, the deconvolution concept was added to DEGAIN.…”

Section: Literature Reviewmentioning

confidence: 99%

Synthetic Financial Data: A Case Study Regarding Polish Limited Liability Companies Data

Szymura

2024

Econometrics

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Aim: The aim of this article was to present and evaluate the concept of synthetic data. They are completely new, artificially generated data, but keep the statistical properties of real data. Due to the statistical similarity with real data, they can be used instead of them. This action allows data to be shared externally while guaranteeing their privacy. Methodology: New datasets were generated based on financial information about Polish limited liability companies, which come from the Orbis database and refer to 2020. To create synthetic data, it was decided to use generative models: CTGAN (based on GAN architecture) and TVAE (based on autoencoders). Lastly, the synthetic data were compared with the real ones in terms of statistical properties (e.g. shape of distributions, correlations etc.) and their applicability in data analysis (the PCA method). Results: The Overall Quality Score was higher for the data generated by TVAE, but after examining the results in more detail, it was seen that the data generated by CTGAN had a better quality in terms of keeping the statistical properties of the real data. Comparing the results of the PCA method, TVAE was better than CTGAN. In addition, the TVAE method was less time-consuming than CTGAN. Implications and recommendations: Before publishing the synthetic data externally, it is recommended that the data are generated using several algorithms, evaluating their final results and finally selecting the best option. This action enables the resulting dataset to be of the highest quality. In further research, it is proposed that other algorithms are tested (e.g. CopulaGAN or TableGAN), in an attempt to deal with some of the realistic data problems that were missed in this analysis, such as missing values (the work was carried out with a complete dataset). Data generated in this study may be used to build financial indicators, which in turn could be used to construct company assessment models. Originality/value: Synthetic data help to deal with some of the data limitations, such as data privacy or scarcity. Due to their statistical similarity with real data, it is possible to use them in advanced machine learning methods instead of real datasets. Analysis on high quality synthetic data allows conclusions similar to analysis on real data to be achieved, while retaining privacy and without publishing sensitive data to third parties.

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Section: Literature Reviewmentioning

confidence: 99%

Synthetic Financial Data: A Case Study Regarding Polish Limited Liability Companies Data

Szymura

2024

Econometrics

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“…The first step we took was data cleaning. This involves identifying and fixing or removing incorrect, corrupt, incorrectly formatted, duplicate, or incomplete data within the dataset [26]. In our dataset, we identified and removed any invalid or missing values, which resulted in a clean dataset with 4700 observations of class 0 and 209 observations of class 1.…”

Section: Data Preprocessingmentioning

confidence: 99%

An Optimization Precise Model of Stroke Data to Improve Stroke Prediction

Ivanov,

Kumchev,

Hooper

2023

Algorithms

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Stroke is a major public health issue with significant economic consequences. This study aims to enhance stroke prediction by addressing imbalanced datasets and algorithmic bias. Our research focuses on accurately and precisely detecting stroke possibility to aid prevention. We tackle the overlooked aspect of imbalanced datasets in the healthcare literature. Our study focuses on predicting stroke in a general context rather than specific subtypes. This clarification will not only ensure a clear understanding of our study’s scope but also enhance the overall transparency and impact of our findings. We construct an optimization model and describe an effective methodology and algorithms for machine learning classification, accommodating missing data and imbalances. Our models outperform previous efforts in stroke prediction, demonstrating higher sensitivity, specificity, accuracy, and precision. Data quality and preprocessing play a crucial role in developing reliable models. The proposed algorithm using SVMs achieves 98% accuracy and 97% recall score. In-depth data analysis and advanced machine learning techniques improve stroke prediction. This research highlights the value of data-oriented approaches, leading to enhanced accuracy and understanding of stroke risk factors. These methods can be applied to other medical domains, benefiting patient care and public health outcomes. By incorporating our findings, the efficiency and effectiveness of the public health system can be improved.

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“…To facilitate this differentiation, the hint generator proffers a 'hint matrix', imparting salient cues to the discriminator regarding the provenance of the data. This ensures the maintenance of a dynamic equilibrium, precluding the generator from perpetually overshadowing the discriminator in performance [65][66][67].…”

Section: Data Pre-processingmentioning

confidence: 99%

Towards Optimal Solar Energy Integration: A Deep Dive into AI-Enhanced Solar Irradiance Forecasting Models

Hanif,

Naveed,

et al. 2023

Preprint

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DEGAIN: Generative-Adversarial-Network-Based Missing Data Imputation

Cited by 13 publications

References 38 publications

Synthetic Financial Data: A Case Study Regarding Polish Limited Liability Companies Data

Synthetic Financial Data: A Case Study Regarding Polish Limited Liability Companies Data

An Optimization Precise Model of Stroke Data to Improve Stroke Prediction

Towards Optimal Solar Energy Integration: A Deep Dive into AI-Enhanced Solar Irradiance Forecasting Models

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