one-class classificationclassifier ensembleensemble pruningclassifier selectiondiversityOne-class classification is considered as one of the most challenging topics in the contemporary machine learning. Creating Multiple Classifier Systems for this task has proven itself as a promising research direction. Here ...
One-class classification algorithms can be used for binary classification tasks with a severely skewed class distribution. These techniques can be fit on the input examples from the majority class in the training dataset, then evaluated on a holdout test dataset. Although not designed for these typ...
Algorithms found in the literature spread over a wide range of applications where ensembles of one-class classifiers have been satisfactorily applied; however, none is oriented to the area under our study: personal risk detection. OCKRA has been designed with the aim of improving the detection ...
iotpapersvmoutlier-detectionsvm-learningaaaionline-learningonline-algorithmsanomaly-detectiongaussian-kernelonline-learning-algorithmsone-class-svmsvddone-class-classificationoutlier-detection-algorithmaaai2019aaai19aaai-19support-vectors UpdatedSep 15, 2020 ...
As class-imbalance problems are typical in protein classification tasks, we were interested in testing one-class classification algorithms for the detection of distant similarities in protein sequences and structures. We found that the OCC approach brought about a small improvement in classification ...
In particular, one-class classification algorithms have gained the interest of the researchers when the available samples in the training set refer to a unique/single class. In this paper, we propose a simple one-class classification approach based on the Mahalanobis distance. We make use of the...
One-class classification. PhD thesis, Technische Universiteit Delft, 2001. 12. L. I. Kuncheva. Combining Pattern Classifiers: Methods and Algorithms. Wiley, 2004. 13. C. Elkan. Results of the KDD 99 Classifier Learning. ACM SIGKDD Explorations, 2000, Vol. 1(2), 63-64. ...
One-class classification problem has been investigated thoroughly for past decades. Among one of the most effective neural network approaches for one-class classification, autoencoder has been successfully applied for many applications. However, this classifier relies on traditional learning algorithms such...
The expression of g-mean is given by Wu and Ye [37]g=a+×a−,where a+ and a− denote the classification accuracy rates of a certain Conclusion There are two stages to construct the proposed one-class classifier. First, the sparse coefficient vectors are obtained by the proposed robust...
One-class classification aims at constructing a distinctive classifier based on one class of examples. Most of the existing one-class classification methods are proposed based on the assumptions that: (1) there are a large number of training examples available for learning the classifier; (2) the...