Synthetic Data Approach for Classification and Regression

Yue, Yang; Li, Ying; Yi, Kexin; Wu, Zhonghai

doi:10.1109/asap.2018.8445094

Cited by 8 publications

(2 citation statements)

References 5 publications

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“…The parameters sharing the same value were not used for modeling to prevent modeling error. To maximize the performance of ML models, we applied DataSynthesizer , the Python library for generating synthetic data, to make a total of 200 data from 32 experimental data. , In this study, SAC was predicted, and the remaining data were used as input variables for the SAC prediction. The details of process data are described in Table , and raw data collected from previous studies are shown in Table S1.…”

Section: Methodsmentioning

confidence: 99%

Optimal Management Strategy for Salt Adsorption Capacity in Machine Learning-Based Flow-Electrode Capacitive Deionization Process

Yu,

Jeon,

Shin

et al. 2024

ACS EST Eng.

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Flow-electrode capacitive deionization (FCDI) has created a breakthrough toward a more stable desalination performance by adopting a flow-electrode compared to existing capacitive deionization and membrane capacitive deionization as a promising electrochemical water treatment technology. However, the FCDI technology requires investigation of various mechanisms pertaining to flow-electrode materials to achieve system optimization. Further, studies on applying machine learning to the FCDI technology have been scarcely reported. Our study aims to explore optimal algorithms via machine learning for predicting the salt adsorption capacity of FCDI processes and evaluate the feasibility of optimization applications. Concurrently, a comparative analysis was conducted through the performance model indicators of mean absolute error (MAE), mean squared error, and R 2 for support vector machine, random forest, and artificial neural network (ANN) algorithms. Herein, we demonstrated that the optimal ANNbased model exhibited the highest predictive performance, achieving R 2 and MAE values of 0.996 and 0.21 mg/g, respectively. Additionally, the Shapley additive explanations (SHAP) confirmed a trend in the contribution of influent concentration, aligning closely with the results of statistical analysis. Specifically, the change in voltage of the FCDI process serves as a key factor in determining salt adsorption efficiency. Moreover, a parallel comparison of the Pearson correlation coefficient and SHAP analyses suggests that the impact of voltage entails a nonlinear contribution within the realm of machine learning. Finally, to deploy a machine learning-driven ANN model system, we present multiple factors (e.g., weight of flow-electrodes, influent concentration, and voltages) as a reinforcement learning model for decision-making. This offers valuable insights and guidance for future operations of the FCDI process.

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Section: Methodsmentioning

confidence: 99%

Optimal Management Strategy for Salt Adsorption Capacity in Machine Learning-Based Flow-Electrode Capacitive Deionization Process

Yu,

Jeon,

Shin

et al. 2024

ACS EST Eng.

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“…In microarray technology, thousands of gene expressions are produced under few conditions' samples. The inadequate number of condition samples yield a faulty generalization and an inaccurate precision of classification models [14]. Data augmentation, the synthetic generation of additional training samples, can help in resolving the imbalance in data [15,16].…”

Section: Introductionmentioning

confidence: 99%

Classical and Deep Learning Paradigms for Detection and Validation of Key Genes of Risky Outcomes of HCV

Samee

2020

Algorithms

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Hepatitis C virus (HCV) is one of the most dangerous viruses worldwide. It is the foremost cause of the hepatic cirrhosis, and hepatocellular carcinoma, HCC. Detecting new key genes that play a role in the growth of HCC in HCV patients using machine learning techniques paves the way for producing accurate antivirals. In this work, there are two phases: detecting the up/downregulated genes using classical univariate and multivariate feature selection methods, and validating the retrieved list of genes using Insilico classifiers. However, the classification algorithms in the medical domain frequently suffer from a deficiency of training cases. Therefore, a deep neural network approach is proposed here to validate the significance of the retrieved genes in classifying the HCV-infected samples from the disinfected ones. The validation model is based on the artificial generation of new examples from the retrieved genes’ expressions using sparse autoencoders. Subsequently, the generated genes’ expressions data are used to train conventional classifiers. Our results in the first phase yielded a better retrieval of significant genes using Principal Component Analysis (PCA), a multivariate approach. The retrieved list of genes using PCA had a higher number of HCC biomarkers compared to the ones retrieved from the univariate methods. In the second phase, the classification accuracy can reveal the relevance of the extracted key genes in classifying the HCV-infected and disinfected samples.

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Can We Trust Synthetic Data in Medicine? A Scoping Review of Privacy and Utility Metrics

Kaabachi,

Despraz,

Meurers

et al. 2023

Preprint

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IntroductionSharing and re-using health-related data beyond the scope of its initial collection is essential for accelerating research, developing robust and trustworthy machine learning algorithms methods that can be translated into clinical settings. The sharing of synthetic data, artificially generated to resemble real patient data, is increasingly recognized as a promising means to enable such a re-use while addressing the privacy concerns related to personal medical data. Nonetheless, no consensus exists yet on a standard approach for systematically and quantitatively evaluating the actual privacy gain and residual utility of synthetic data, de-facto hindering its adoption.ObjectiveIn this work, we present and systematize current knowledge on the field of synthetic health-related data evaluation both in terms of privacy and utility. We provide insights and critical analysis into the current state of the art and propose concrete directions and steps forward for the research community.MethodsWe assess and contextualize existing knowledge in the field through a scoping review and the creation of a common ontology that encompasses all the methods and metrics used to assess synthetic data. We follow the PRISMA-ScR methodology in order to perform data collection and knowledge synthesis.ResultsWe include 92 studies in the scoping review. We analyze and classify them according to the proposed ontology. We found 48 different methods to evaluate the residual statistical utility of synthetic data and 9 methods that are used to evaluate the residual privacy risks. Moreover, we observe that there is currently no consensus among researchers regarding neither individual metrics nor family of metrics for evaluating the privacy and utility of synthetic data. Our findings on the privacy of synthetic data show that there is an alarming tendency to trust the safety of synthetic data without properly evaluating it.ConclusionAlthough the use of synthetic data in healthcare promises to offer an easy and hassle-free alternative to real data, the lack of consensus in terms of evaluation hinders the adoption of this new technology. We believe that, by raising awareness and providing a comprehensive taxonomy on evaluation methods that takes into account the current state of literature, our work can foster the development and adoption of uniform approaches and consequently facilitate the use of synthetic data in the medical domain.

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Synthetic Data Approach for Classification and Regression

Cited by 8 publications

References 5 publications

Optimal Management Strategy for Salt Adsorption Capacity in Machine Learning-Based Flow-Electrode Capacitive Deionization Process

Optimal Management Strategy for Salt Adsorption Capacity in Machine Learning-Based Flow-Electrode Capacitive Deionization Process

Classical and Deep Learning Paradigms for Detection and Validation of Key Genes of Risky Outcomes of HCV

Can We Trust Synthetic Data in Medicine? A Scoping Review of Privacy and Utility Metrics

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