[HTML][HTML] Groundwater level prediction using machine learning models: A comprehensive review
Developing accurate soft computing methods for groundwater level (GWL) forecasting is
essential for enhancing the planning and management of water resources. Over the past two …
essential for enhancing the planning and management of water resources. Over the past two …
[PDF][PDF] Counterfactual explanations for machine learning: A review
Abstract Machine learning plays a role in many deployed decision systems, often in ways
that are difficult or impossible to understand by human stakeholders. Explaining, in a human …
that are difficult or impossible to understand by human stakeholders. Explaining, in a human …
A review of irregular time series data handling with gated recurrent neural networks
Irregular time series data is becoming increasingly prevalent with the growth of multi-sensor
systems as well as the continued use of unstructured manual data recording mechanisms …
systems as well as the continued use of unstructured manual data recording mechanisms …
Stacked bidirectional and unidirectional LSTM recurrent neural network for forecasting network-wide traffic state with missing values
Short-term traffic forecasting based on deep learning methods, especially recurrent neural
networks (RNN), has received much attention in recent years. However, the potential of RNN …
networks (RNN), has received much attention in recent years. However, the potential of RNN …
Gain: Missing data imputation using generative adversarial nets
We propose a novel method for imputing missing data by adapting the well-known
Generative Adversarial Nets (GAN) framework. Accordingly, we call our method Generative …
Generative Adversarial Nets (GAN) framework. Accordingly, we call our method Generative …
Missing value imputation: a review and analysis of the literature (2006–2017)
WC Lin, CF Tsai - Artificial Intelligence Review, 2020 - Springer
Missing value imputation (MVI) has been studied for several decades being the basic
solution method for incomplete dataset problems, specifically those where some data …
solution method for incomplete dataset problems, specifically those where some data …
Multivariate time series imputation with generative adversarial networks
Multivariate time series usually contain a large number of missing values, which hinders the
application of advanced analysis methods on multivariate time series data. Conventional …
application of advanced analysis methods on multivariate time series data. Conventional …
Counterfactual explanations and algorithmic recourses for machine learning: A review
Machine learning plays a role in many deployed decision systems, often in ways that are
difficult or impossible to understand by human stakeholders. Explaining, in a human …
difficult or impossible to understand by human stakeholders. Explaining, in a human …
Recurrent neural networks for multivariate time series with missing values
Multivariate time series data in practical applications, such as health care, geoscience, and
biology, are characterized by a variety of missing values. In time series prediction and other …
biology, are characterized by a variety of missing values. In time series prediction and other …
Handling missing data with graph representation learning
Abstract Machine learning with missing data has been approached in many different ways,
including feature imputation where missing feature values are estimated based on observed …
including feature imputation where missing feature values are estimated based on observed …