Synthetic data generation for tabular health records: A systematic review
Synthetic data generation (SDG) research has been ongoing for some time with promising
results in different application domains, including healthcare, biometrics and energy …
results in different application domains, including healthcare, biometrics and energy …
Method evaluation, parameterization, and result validation in unsupervised data mining: A critical survey
A Zimmermann - Wiley Interdisciplinary Reviews: Data Mining …, 2020 - Wiley Online Library
Abstract Machine Learning (ML) and Data Mining (DM) build tools intended to help users
solve data‐related problems that are infeasible for “unaugmented” humans. Tools need …
solve data‐related problems that are infeasible for “unaugmented” humans. Tools need …
[HTML][HTML] Synthetic tabular data evaluation in the health domain covering resemblance, utility, and privacy dimensions
Background Synthetic tabular data generation is a potentially valuable technology with great
promise for data augmentation and privacy preservation. However, prior to adoption, an …
promise for data augmentation and privacy preservation. However, prior to adoption, an …
Exploring city digital twins as policy tools: A task-based approach to generating synthetic data on urban mobility
G Papyshev, M Yarime - Data & Policy, 2021 - cambridge.org
This article discusses the technology of city digital twins (CDTs) and its potential applications
in the policymaking context. The article analyzes the history of the development of the …
in the policymaking context. The article analyzes the history of the development of the …
Standardised metrics and methods for synthetic tabular data evaluation
Synthetic Tabular Data Generation (STDG) is a potentially valuable technology with great
promise to augment real data and preserve privacy. However, prior to adoption, an empirical …
promise to augment real data and preserve privacy. However, prior to adoption, an empirical …
Gensyn: A multi-stage framework for generating synthetic microdata using macro data sources
Individual-level data (microdata) that characterizes a population, is essential for studying
many real-world problems. However, acquiring such data is not straightforward due to cost …
many real-world problems. However, acquiring such data is not straightforward due to cost …
Machine learning methods for generating high dimensional discrete datasets
The development of platforms and techniques for emerging Big Data and Machine Learning
applications requires the availability of real‐life datasets. A possible solution is to synthesize …
applications requires the availability of real‐life datasets. A possible solution is to synthesize …
[HTML][HTML] Making a few talk for the many–Modeling driver behavior using synthetic populations generated from experimental data
R Schindler, C Flannagan, A Bálint… - Accident Analysis & …, 2021 - Elsevier
Understanding driver behavior is the basis for the development of many advanced driver
assistance systems, and experimental studies are indispensable tools for constructing …
assistance systems, and experimental studies are indispensable tools for constructing …
An economic feasibility assessment framework for underutilised crops using Support Vector Machine
As susceptibility of commercial crops to the changing climates and resulting harsher
conditions increases, interest in the potential of resilient underutilised crops grows …
conditions increases, interest in the potential of resilient underutilised crops grows …
The relaxed maximum entropy distribution and its application to pattern discovery
S Dalleiger, J Vreeken - 2020 IEEE International Conference …, 2020 - ieeexplore.ieee.org
The maximum entropy principle uniquely identifies the distribution that models our
knowledge about the data, but is otherwise maximally unbiased. As soon as we include non …
knowledge about the data, but is otherwise maximally unbiased. As soon as we include non …