ROC analysis of classifiers in machine learning: A survey

Article type: Research Article

Authors: Majnik, Matjaž^{; *} | Bosnić, Zoran

Affiliations: Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia

Correspondence: [*] Corresponding author: Matjaž Majnik, Faculty of Computer and Information Science, University of Ljubljana, Tržaška 25, Ljubljana, Slovenia. Tel.: +386 1 4768459; Fax: +386 1 4768498; E-mail: matjaz.majnik@fri.uni-lj.si.

Abstract: The use of ROC (Receiver Operating Characteristics) analysis as a tool for evaluating the performance of classification models in machine learning has been increasing in the last decade. Among the most notable advances in this area are the extension of two-class ROC analysis to the multi-class case as well as the employment of ROC analysis in cost-sensitive learning. Methods now exist which take instance-varying costs into account. The purpose of our paper is to present a survey of this field with the aim of gathering important achievements in one place. In the paper, we present application areas of the ROC analysis in machine learning, describe its problems and challenges and provide a summarized list of alternative approaches to ROC analysis. In addition to presented theory, we also provide a couple of examples intended to illustrate the described approaches.

Keywords: ROC analysis, ROC, performance, machine learning, classification

DOI: 10.3233/IDA-130592

Journal: Intelligent Data Analysis, vol. 17, no. 3, pp. 531-558, 2013