A Binary Decision Diagram-Based One-Class Classifier

Abstract

We propose a novel approach for one-class classification problems where a logical formula is used to estimate the region that covers all examples. A formula is viewed as a model that represents a region and is approximated with respect to its hierarchical local densities. The approximation is done quite efficiently via direct manipulations of a binary decision diagram that is a compressed representation of a Boolean formula. The proposed method has only one parameter to be tuned, and the parameter can be selected properly with the help of the minimum description length principle, which requires no labeled training data. In other words, a one-class classifier is generated from an unlabeled training data thoroughly and automatically. Experimental results show that the proposed method works quite well with synthetic data and some realistic data.