One-class classification: taxonomy of study and review of techniques
One-class classification (OCC) algorithms aim to build classification models when the
negative class is either absent, poorly sampled or not well defined. This unique situation …
negative class is either absent, poorly sampled or not well defined. This unique situation …
A survey of recent trends in one class classification
Abstract 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 …
binary/multi-class classification problem in the sense that in OCC, the negative class is …
A literature review on one-class classification and its potential applications in big data
In severely imbalanced datasets, using traditional binary or multi-class classification typically
leads to bias towards the class (es) with the much larger number of instances. Under such …
leads to bias towards the class (es) with the much larger number of instances. Under such …
ROC analysis
PA Flach - … of machine learning and data mining, 2016 - research-information.bris.ac.uk
ROC analysis investigates and employs the relationship between sensitivity and specificity
of a binary classifier. Sensitivity or true positiverate measures the proportion of positives …
of a binary classifier. Sensitivity or true positiverate measures the proportion of positives …
Multiclass from binary: Expanding one-versus-all, one-versus-one and ecoc-based approaches
A Rocha, SK Goldenstein - IEEE transactions on neural …, 2013 - ieeexplore.ieee.org
Recently, there has been a lot of success in the development of effective binary classifiers.
Although many statistical classification techniques have natural multiclass extensions, some …
Although many statistical classification techniques have natural multiclass extensions, some …
One-class classification: Concept learning in the absence of counter-examples.
DMJ Tax - 2002 - elibrary.ru
Degree: Dr. DegreeYear: 2001 Institute: Technische Universiteit Delft (The Netherlands)
Publisher: Print partners Ipskamp, Capitool 25, Postbus 333, 7500 AH Enschede, The …
Publisher: Print partners Ipskamp, Capitool 25, Postbus 333, 7500 AH Enschede, The …
A three-way clustering approach for novelty detection
Novelty detection aims to identify novel instances in the test data that differ in some respect
from the normal instances in the training data. Novel instances may be defined and …
from the normal instances in the training data. Novel instances may be defined and …
Measuring classifier performance: a coherent alternative to the area under the ROC curve
DJ Hand - Machine learning, 2009 - Springer
The area under the ROC curve (AUC) is a very widely used measure of performance for
classification and diagnostic rules. It has the appealing property of being objective, requiring …
classification and diagnostic rules. It has the appealing property of being objective, requiring …
A mixture model and EM-based algorithm for class discovery, robust classification, and outlier rejection in mixed labeled/unlabeled data sets
DJ Miller, J Browning - IEEE Transactions on Pattern Analysis …, 2003 - ieeexplore.ieee.org
Several authors have shown that, when labeled data are scarce, improved classifiers can be
built by augmenting the training set with a large set of unlabeled examples and then …
built by augmenting the training set with a large set of unlabeled examples and then …
One-class support vector classifiers: A survey
S Alam, SK Sonbhadra, S Agarwal… - Knowledge-Based …, 2020 - Elsevier
Over the past two decades, one-class classification (OCC) becomes very popular due to its
diversified applicability in data mining and pattern recognition problems. Concerning to …
diversified applicability in data mining and pattern recognition problems. Concerning to …