A Survey of Recent Trends in One Class Classification

By Shehroz S. Khan, Michael G. Madden. 2009

446 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

Citations in the corpus

One-Class Learning Towards Synthetic Voice Spoofing Detection (2020, background): “In the one-class classification setup [23], there is a target class that does not have this distribution mismatch prob-”
Generalized Out-of-Distribution Detection: A Survey (2021, unknown): “The idea of classifier boundaries is successfully implemented and marked as a one-class classification task [252], [336], which we describe in Section 5.”
Social Media Text Classification under Negative Covariate Shift (2015, background): “Also, as pointed out by (Khan and Madden, 2010; 2014), one drawback of their methods is that they often require a large dataset and the methods become very inefficient in high dimensional feature spaces.”
A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation (2023, background): “A survey of recent trends in OCC has been presented by Khan et al. [278].”
One-class classification: taxonomy of study and review of techniques (2013, unknown): “Khan and Madden (2009) present a short survey of the recent trends in the field of OCC, wherein they present a taxonomy for the study of OCC methods.”
A review of novelty detection (2014, background): “A number of surveys have been published since then [26–32,24], but none of these attempts to be as wideranging as we are in our review.”
Improving Confidence Estimates for Unfamiliar Examples (2018, unknown): “Other methods aim to reduce conﬁdent errors by detecting failure [8, 10, 55, 51, 44], for example by looking at how close samples are to decision boundaries or estimating whether a test sample comes from the same distribution as training [5, 25, 28, 48, 22].”