A classification model based on a population of hypergraphs
This addresses a limitation in hypergraph classification for data analysis, though it appears incremental as it builds on existing hypergraph methods.
The paper tackles the problem of capturing multi-way interactions in hypergraph classification by constructing hypergraphs that explore interactions of any order and using a population of hypergraphs to improve performance and robustness. It demonstrates promising performance compared to a generic random forest on two datasets.
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.