  One of the most rising issues in recent machine learning research is
One-Class Classification which considers data set composed of only one class
and outliers. It is more reasonable than traditional Multi-Class Classification
in dealing with some problematic data set or special cases. Generally,
classification accuracy and interpretability for user are considered as
trade-off in OCC methods. Classifier based on Hyper-Rectangle (H-RTGL) is a
sort of classifier that can be a remedy for such trade-off and uses H-RTGL
formulated by conjunction of geometric rules called interval. This interval can
be basis of interpretability since it can be easily understood by user.
However, existing H-RTGL based OCC classifiers have limitations that (i) most
of them cannot reflect density of target class and (ii) that considering
density has primitive interval generation method, and (iii) there exists no
systematic procedure for hyperparameter of H-RTGL based OCC classifier, which
influences classification performance of classifier. Based on these remarks, we
suggest One-Class Hyper-Rectangle Descriptor based on density (1-HRD_d) with
more elaborate interval generation method including parametric and
nonparametric approaches. In addition, we designed Genetic Algorithm (GA) that
consists of chromosome structure and genetic operators for systematic
generation of 1-HRD_d by optimization of hyperparameter. Our work is validated
through a numerical experiment using actual data set with comparison of
existing OCC algorithms along with other H-RTGL based classifiers.
