Use of administrative data for research and for planning services has increased over recent
decades due to the value of the large, rich information available. However, concerns about …

The presence of missing values in real-world data is not only a prevalent problem but also
an inevitable one. Therefore, missing values should be handled carefully before the mining …

Bayesian networks can be used to identify possible causal relationships between variables
based on their conditional dependencies and independencies, which can be particularly …

In this study we describe the optimal designation of agri-environmental policy as a bilevel
optimization problem and propose an integrated solution method using a hybrid genetic …

Missing values are an important problem in data mining. In order to tackle this problem in
classification tasks, we propose two imputation methods based on Bayesian networks …

Missing values are a common occurrence in a number of real world databases, and
statistical methods have been developed to deal with this problem, referred to as missing …

A Bayesian network is a graphical model of the joint probability distribution for a set of
variables. A Bayesian network could be used to create multiple synthetic data sets that are …

During the process of data collection, incompleteness is one of the most serious data quality
problems to deal with. Traditional imputation methods mostly rely on statistics and machine …

This study evaluates the tradeoff between agricultural production and water quality at both
the watershed scale and the farm scale, using an integrated economic-biophysical hybrid …

The assessment of the health impacts of the COVID-19 pandemic requires the consideration
of mobility networks. To this aim, we propose to augment spatio-temporal point process …