  This paper introduces a novel hypergraph classification algorithm. The use of
hypergraphs in this framework has been widely studied. In previous work,
hypergraph models are typically constructed using distance or attribute based
methods. That is, hyperedges are generated by connecting a set of samples which
are within a certain distance or have a common attribute. These methods
however, do not often focus on multi-way interactions directly. The algorithm
provided in this paper looks to address this problem by constructing
hypergraphs which explore multi-way interactions of any order. We also increase
the performance and robustness of the algorithm by using a population of
hypergraphs. The algorithm is evaluated on two datasets, demonstrating
promising performance compared to a generic random forest classification
algorithm.
