A Review of Supervised Classification based on Contrast Patterns: Applications, Trends, and Challenges
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A Review of Supervised Classification based on Contrast Patterns: Applications, Trends, and Challenges Octavio Loyola-Gonz´alez · Miguel Angel Medina-P´erez · Kim-Kwang Raymond Choo
Received: 16 August 2019 / Accepted: 19 June 2020 © Springer Nature B.V. 2020
Abstract Supervised classification based on Contrast Patterns (CP) is a trending topic in the pattern recognition literature, partly because it contains an important family of both understandable and accurate classifiers. In this paper, we survey 105 articles and provide an in-depth review of CP-based supervised classification and its applications. Based on our review, we present a taxonomy of the existing application domains of CP-based supervised classification, and a scientometric study. We also discuss potential future research opportunities. Keywords Supervised classification · Contrast patterns · Review · Taxonomy
O. Loyola-Gonz´alez () Tecnologico de Monterrey, Reserva Territorial Atlixc´ayotl, V´ıa Atlixc´ayotl No. 2301, Puebla 72453, Mexico e-mail: [email protected] M. A. Medina-P´erez Tecnologico de Monterrey, Carretera al Lago de Guadalupe Km. 3.5, Atizap´an, Estado de Mexico 52926, M´exico e-mail: [email protected] K.-K. R. Choo Department of Information Systems and Cyber Security, University of Texas at San Antonio, San Antonio, TX 78249, USA e-mail: [email protected]
1 Introduction Artificial intelligence (e.g., machine learning and deep learning) approaches are increasingly popular in the literature [3, 140, 164, 166, 207]. Examples of machine learning and deep learning approaches include random forests, gradient boosting, convolutional neural networks, and pattern recognition. Supervised classification is one of the most popular pattern recognition approaches [32, 119], which has been widely studied and applied to many domains, such as bioinformatics [13, 84, 203], human activity recognition [94, 120, 146, 190], rare event forecasting [34, 78, 162], information retrieval [18, 30, 171, 191], face recognition [9, 134, 135], fingerprint identification [79, 143], Internet of Things [8, 202], and more recently COVID-19 (also referred to as novel Coronavirus, 2019-nCOV and SARS-CoV-2) [138, 182]. Contrast pattern-based classification continues to attract interest from the research and practitioner communities since its introduction in 1963, as evidenced by the number of supervised classifiers proposed over the past decade. However, for many practical problems, obtaining a high-classification result is insufficient, because experts should also understand the classification model [46, 119, 127]. In many application domains, the lack of comprehensibility in the classification model(s) can result in resistance or reluctance to use certain classifiers, and in other cases, it becomes mandatory to use an understandable model. For example, when credit is denied to a customer, the U.S.’s
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