A viral outbreak is a global challenge that affects public health and safety. The coronavirus
disease 2019 (COVID-19) has been spreading globally, affecting millions of people …

Subgroup discovery is a data mining technique which extracts interesting rules with respect
to a target variable. An important characteristic of this task is the combination of predictive …

One of the most important obstacles to deploying predictive models is the fact that humans
do not understand and trust them. Knowing which variables are important in a model's …

Evolutionary fuzzy systems are one of the greatest advances within the area of
computational intelligence. They consist of evolutionary algorithms applied to the design of …

Rules–the clearest, most explored and best understood form of knowledge representation–
are particularly important for data mining, as they offer the best tradeoff between human and …

Existing research is unclear on how to generate lessons learned for defect prediction and
effort estimation. Should we seek lessons that are global to multiple projects or just local to …

Subgroup discovery is a broadly applicable descriptive data mining technique for identifying
interesting subgroups according to some property of interest. This article summarizes …

Discovering frequent patterns from data is a popular exploratory technique in datamining.
However, if the data are sensitive (eg, patient health records, user behavior records) …

We implemented a machine learning approach to investigate individual indicators of training
load and wellness that may predict the emergence or development of overuse injuries in …

Component-based software engineering is a powerful paradigm for building large
applications. However, our experience with building application of components is that the …