Minimum variance-embedded deep kernel regularized least squares method for one-class classification and its applications to biomedical data
Authors: Gautam, C., Mishra, P.K., Tiwari, A., Richhariya, B., Pandey, H.M., Wang, S. and Tanveer, M.
Journal: Neural Networks
Volume: 123
Pages: 191-216
eISSN: 1879-2782
ISSN: 0893-6080
DOI: 10.1016/j.neunet.2019.12.001
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% F1 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.
Source: Scopus
Minimum variance-embedded deep kernel regularized least squares method for one-class classification and its applications to biomedical data.
Authors: Gautam, C., Mishra, P.K., Tiwari, A., Richhariya, B., Pandey, H.M., Wang, S., Tanveer, M. and Alzheimer’s Disease Neuroimaging Initiative
Journal: Neural Netw
Volume: 123
Pages: 191-216
eISSN: 1879-2782
DOI: 10.1016/j.neunet.2019.12.001
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% F1 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.
Source: PubMed
Minimum variance-embedded deep kernel regularized least squares method for one-class classification and its applications to biomedical data
Authors: Gautam, C., Mishra, P.K., Tiwari, A., Richhariya, B., Pandey, H.M., Wang, S. and Tanveer, M.
Journal: NEURAL NETWORKS
Volume: 123
Pages: 191-216
eISSN: 1879-2782
ISSN: 0893-6080
DOI: 10.1016/j.neunet.2019.12.001
Source: Web of Science (Lite)
Minimum variance-embedded deep kernel regularized least squares method for one-class classification and its applications to biomedical data.
Authors: Gautam, C., Mishra, P.K., Tiwari, A., Richhariya, B., Pandey, H.M., Wang, S., Tanveer, M. and Alzheimer’s Disease Neuroimaging Initiative
Journal: Neural networks : the official journal of the International Neural Network Society
Volume: 123
Pages: 191-216
eISSN: 1879-2782
ISSN: 0893-6080
DOI: 10.1016/j.neunet.2019.12.001
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% F1 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.
Source: Europe PubMed Central