A Survey of Recent Trends in One Class Classification

作者：Shehroz S. Khan, Michael G. Madden · 发表于：Lecture notes in computer science · 年份：2010 · DOI：10.1007/978-3-642-17080-5_21 · 被引用次数：338 · 研究领域：Imbalanced Data Classification Techniques、Text and Document Classification Technologies、Machine Learning and Algorithms

The One Class Classification (OCC) problem is different from the conventional binary/multi-class classification problem in the sense that in OCC, the negative class is either not present or not properly sampled. The problem of classifying positive (or target) cases in the absence of appropriately-characterized negative cases (or outliers) has gained increasing attention in recent years. Researchers have addressed the task of OCC by using different methodologies in a variety of application domains. In this paper we formulate a taxonomy with three main categories based on the way OCC has been envisaged, implemented and applied by various researchers in different application domains. We also present a survey of current state-of-the-art OCC algorithms, their importance, applications and limitations.