Research Article One-Class Classification by Ensembles of Random Planes (OCCERPs) Amir Ahmad College of Information Technology, United Arab Emirates University, Al-Ain, UAE Correspondence should be addressed to Amir Ahmad; amirahmad@uaeu.ac.ae Received 9 February 2022; Revised 11 April 2022; Accepted 26 May 2022; Published 4 July 2022 Academic Editor: Amparo Alonso-Betanzos Copyright © 2022 Amir Ahmad. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. One-class classification (OCC) deals with the classification problem in which the training data have data points belonging only to the target class. In this paper, we present a one-class classification algorithm, One-Class Classification by Ensembles of Random Plane (OCCERP), that uses random planes to address OCC problems. OCCERP creates many random planes. ere is a pivot point in each random plane. A data point is projected in a random plane and a distance from a pivot point is used to compute the outlier score of the data point. Outlier scores of a point computed using many random planes are combined to get the final outlier score of the point. An extensive comparison of the OCCERP algorithm with state-of-the-art OCC algorithms on several datasets was conducted to show the effectiveness of the proposed approach. e effect of the ensemble size on the performance of the OCCERP algorithm is also studied. 1. Introduction e one-class classification (OCC) problem is a special class of classification problems in which only the data points of one class (the target set) are available [1]. e task in one-class classification is to make a model of a target set of data points and to predict if a testing data point is similar to the target set. e point which is not similar to the target set is called an outlier. OCC algo- rithms have applications in various domains [2] including anomaly detection, fraud detection, machine fault de- tection, and spam detection [2]. e OCC problem is generally considered to be a more difficult problem than the two-class classification problem as the training data have only data points belonging to one class [1–3], and traditional classifiers need training data from more than one class to learn decision boundaries. erefore, standard classifiers cannot be applied directly to OCC problems. Various algorithms have been proposed to ad- dress OCC problems [1–3]. ere are two main approaches to handle OCC problems [2, 3]. In the first approach, artificial data points for the nontarget class (outlier) are generated and combined with the target data points and then a binary classifier is trained on this new data. In the second approach, target data points are used to create the OCC models [4]. Gaussian models [3], reconstruction-based methods [1–3], nearest neighbours [1, 5], support vector machines [6, 7] clustering based methods [1], and convex hull [8] are some examples of the second approach. Ensembles of accurate and diverse models generally perform better than individual members of ensembles [9]. Ensembles of classification models have been developed to improve the performance of one-class classification models [5, 10–12]. In this paper, we propose an ensemble method, OCCERP, for OCC problems. In this method, we project data points in a random plane. e distance of a data point from a pivotal point in this random plane is used as an outlier score. We can generate various diverse models by selecting different random planes which can be used to create ensembles. Experiments are done to show the ef- fectiveness of the proposed approach. e paper is organised as follows: Section 2 discusses about related work. e OCCER algorithm is presented in Section 3. Section 4 presents experiments and discussion. Hindawi Computational Intelligence and Neuroscience Volume 2022, Article ID 4264393, 7 pages https://doi.org/10.1155/2022/4264393