Neyman-Pearson classification via context trees [Bağlam ağaçları ile Neyman-Pearson sınıflandırması]

Official URL: http://dx.doi.org/10.1109/SIU49456.2020.9302287

In binary classification problems, such as network intrusion detection, class specific observations are unevenly distributed, and the target class (suspicious activity in the network) is rarely observed compared to the non-target (ordinary network operations). Therefore, type I error (prediction of a non-target observation as target) and type II error (failure to detect target) have different consequences. For example, a network security system that generates too many false alarms (type I error), loses its reliability as it will cause rigorous but unnecessary measures to be taken. For this reason, asymmetric class costs should be used in the designed classifiers and the type I error should be controlled directly. To this end, the Neyman-Pearson (NP) classification framework is suitable for binary classification problems under asymmetric error costs, since it minimizes type II error and keeps type I error below a user-specified threshold. In this paper, we propose an NP classification method that solves nonlinear problems via context trees in an online manner. The performance analysis of the proposed method is performed on well-known data sets and the classification behavior is investigated on the basis of ROC (receiver operating characteristics) curves as well as the capability to achieve the desired type I error. According to our analyzes, the proposed algorithm successfully provides an average of 66% increase in the area under the ROC curve along with a precise control over the desired type I error, compared to the algorithms that do not use context trees and can only solve linear problems.