dc.contributor.author |
Richhariya, Bharat |
|
dc.date.accessioned |
2024-05-06T04:04:31Z |
|
dc.date.available |
2024-05-06T04:04:31Z |
|
dc.date.issued |
2020-03 |
|
dc.identifier.uri |
https://www.sciencedirect.com/science/article/pii/S0893608019303934 |
|
dc.identifier.uri |
http://dspace.bits-pilani.ac.in:8080/jspui/xmlui/handle/123456789/14717 |
|
dc.description.abstract |
Deep kernel learning has been well explored for multi-class classification tasks; however, relatively less work is done for one-class classification (OCC). OCC needs samples from only one class to train the model. Most recently, kernel regularized least squares (KRL) method-based deep architecture is developed for the OCC task. This paper introduces a novel extension of this method by embedding minimum variance information within this architecture. This embedding improves the generalization capability of the classifier by reducing the intra-class variance. In contrast to traditional deep learning methods, this method can effectively work with small-size datasets. We conduct a comprehensive set of experiments on 18 benchmark datasets (13 biomedical and 5 other datasets) to demonstrate the performance of the proposed classifier. We compare the results with 16 state-of-the-art one-class classifiers. Further, we also test our method for 2 real-world biomedical datasets viz.; detection of Alzheimer’s disease from structural magnetic resonance imaging data and detection of breast cancer from histopathological images. Proposed method exhibits more than 5% score compared to existing state-of-the-art methods for various biomedical benchmark datasets. This makes it viable for application in biomedical fields where relatively less amount of data is available. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
Elsevier |
en_US |
dc.subject |
Computer Science |
en_US |
dc.subject |
One-class Classification |
en_US |
dc.subject |
Kernel Learning |
en_US |
dc.subject |
Outlier Detection |
en_US |
dc.subject |
Alzheimer’s disease |
en_US |
dc.subject |
Magnetic resonance imaging |
en_US |
dc.title |
Minimum variance-embedded deep kernel regularized least squares method for one-class classification and its applications to biomedical data |
en_US |
dc.type |
Article |
en_US |