  With the rapid development of digital information, the data volume generated
by humans and machines is growing exponentially. Along with this trend, machine
learning algorithms have been formed and evolved continuously to discover new
information and knowledge from different data sources. Learning algorithms
using hyperboxes as fundamental representational and building blocks are a
branch of machine learning methods. These algorithms have enormous potential
for high scalability and online adaptation of predictors built using hyperbox
data representations to the dynamically changing environments and streaming
data. This paper aims to give a comprehensive survey of literature on
hyperbox-based machine learning models. In general, according to the
architecture and characteristic features of the resulting models, the existing
hyperbox-based learning algorithms may be grouped into three major categories:
fuzzy min-max neural networks, hyperbox-based hybrid models, and other
algorithms based on hyperbox representations. Within each of these groups, this
paper shows a brief description of the structure of models, associated learning
algorithms, and an analysis of their advantages and drawbacks. Main
applications of these hyperbox-based models to the real-world problems are also
described in this paper. Finally, we discuss some open problems and identify
potential future research directions in this field.
