Evolving hypernetworks for pattern classification

Hypernetworks consist of a large number of hyperedges that represent higher-order features sampled from training patterns. Evolutionary algorithms have been used as a method for evolving hypernetworks. The order of a hyperedge is defined as the number of feature variables in the hyperedge and it is an important parameter of the hypernetwork model. Previous studies used fixed-order hyperedges which limit model spaces and, thus, the best performance achievable by hypernetworks. Here, we present a method for evolving variable-order hypernetwork models. To find the proper orders automatically, the fitness values are calculated for each hyperedge and the hyperedges with low fitness values are substituted by new hyperedges. The method was tested on three data sets from UCI machine learning repository. The results show that the evolutionary hypernetworks show classification accuracies comparable to those of other conventional algorithms, find appropriate orders of hyperedges automatically, and extract important rules in the hyperedges for the given pattern classification problems.