Density-Based Geometric One-Class Classifier Combined with Genetic Algorithm - Kim, Do Gyun; Choi, Jin Young (Mathematical Problems in Engineering, 2022)
One of the most prospective issues in recent machine learning research is one-class classification (OCC), which considers datasets composed of only one class and outlier. It is more reasonable than the traditional multiclass classification in dealing with problematic datasets or special cases. Generally, classification accuracy and interpretability for users are considered to have a trade-off in OCC methods. A classifier based on hyperrectangle (H-RTGL) can alleviate such a trade-off and uses H-RTGL formulated by the conjunction of geometric rules (called an interval). This interval can form a basis for interpretability since it can be easily understood by the user. However, the existing H-RTGL-based OCC classifiers have the following limitations: (i) they cannot reflect the density of the target class, (ii) the density is considered using a primitive interval generation method, and (iii) there exists no systematic procedure for determining the hyperparameter of the H-RTGL-based OCC classifier, which influences its classification performance. Therefore, we suggest a one-class hyperrectangle descriptor based on density 1-HRDd with a more elaborate interval generation method, including parametric and nonparametric approaches. Specifically, we design a genetic algorithm that comprises a chromosome structure and genetic operators for systematic generation of 1-HRDd through optimization of the hyperparameter. Our study is validated through a numerical experiment using several actual datasets with different sizes and features, and the result is compared to the existing OCC algorithms along with other H-RTGL-based classifiers.
Keywords: Density-based; Generation method; Hyper-parameter; Hyperrectangles; Interpretability; Machine learning research; One-class Classification; One-class classifier; Prospectives; Trade off