Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
Cluster Ensemble Kernel for Kernel-based Classification. / Odinokikh, Nikita; Berikov, Vladimir.
SIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. стр. 670-674 8958184 (SIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings).Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
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TY - GEN
T1 - Cluster Ensemble Kernel for Kernel-based Classification
AU - Odinokikh, Nikita
AU - Berikov, Vladimir
N1 - Funding Information: ACKNOWLEDGMENT The work was partly supported by RFBR grants 18-07-00600 and 19-29-01175.
PY - 2019/10
Y1 - 2019/10
N2 - This paper presents a method for some semi-supervised and supervised classification problems based on properties of the averaged co-Association matrix obtained with a cluster ensemble. The ensemble clustering is performed as a preliminary step of data processing. The main property states that the matrix is a valid kernel matrix, thus it can be used in different classification methods that use kernels such as Kernel Nearest Neighbor, SVM, Kernel Fisher Discriminant. Some properties of the suggested method connected with its convergence to optimal classifier are studied. Numerical experiments show that the accuracy of the proposed algorithms is often higher than other state-of-The-Art methods, especially under the presence of complex data structures and noise distortions.
AB - This paper presents a method for some semi-supervised and supervised classification problems based on properties of the averaged co-Association matrix obtained with a cluster ensemble. The ensemble clustering is performed as a preliminary step of data processing. The main property states that the matrix is a valid kernel matrix, thus it can be used in different classification methods that use kernels such as Kernel Nearest Neighbor, SVM, Kernel Fisher Discriminant. Some properties of the suggested method connected with its convergence to optimal classifier are studied. Numerical experiments show that the accuracy of the proposed algorithms is often higher than other state-of-The-Art methods, especially under the presence of complex data structures and noise distortions.
KW - cluster ensemble
KW - co-Association matrix
KW - kernel-based classification
UR - http://www.scopus.com/inward/record.url?scp=85079047330&partnerID=8YFLogxK
UR - https://elibrary.ru/item.asp?id=43237103
U2 - 10.1109/SIBIRCON48586.2019.8958184
DO - 10.1109/SIBIRCON48586.2019.8958184
M3 - Conference contribution
AN - SCOPUS:85079047330
T3 - SIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings
SP - 670
EP - 674
BT - SIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2019
Y2 - 21 October 2019 through 27 October 2019
ER -
ID: 27889733