TY - JOUR

T1 - Searching for optimal classifier using a combination of cluster ensemble and kernel method

AU - Berikov, Vladimir B.

AU - Cherikbayeva, Lyailya Sh

N1 - Publisher Copyright: Copyright © by the paper's authors.

PY - 2018/1/1

Y1 - 2018/1/1

N2 - This work introduces a supervised classification algorithm based on a combination of ensemble clustering and kernel method. The main idea of the algorithm lies behind the expectation that the ensemble clustering as a preliminary stage would restore more accurately metric relations between data objects under noise distortions and existence of complex data structures, eventually rising the overall classification quality. The algorithm consists in two major steps. On the first step, the averaged co-association matrix is calculated using cluster ensemble. It is proved that the matrix satisfies Mercer's condition, i.e., it defines symmetric non-negative definite kernel. On the next step, optimal classifier is found with the obtained kernel matrix as input. The classifier maximizes the width of hyperplane's separation margin in the space induced by the cluster ensemble kernel. Numerical experiments with artificial examples and real hyperspectral image have shown that the proposed algorithm possesses classification accuracy comparable with some state-of-the-art methods, and in many cases outperforms them, especially in noise conditions.

AB - This work introduces a supervised classification algorithm based on a combination of ensemble clustering and kernel method. The main idea of the algorithm lies behind the expectation that the ensemble clustering as a preliminary stage would restore more accurately metric relations between data objects under noise distortions and existence of complex data structures, eventually rising the overall classification quality. The algorithm consists in two major steps. On the first step, the averaged co-association matrix is calculated using cluster ensemble. It is proved that the matrix satisfies Mercer's condition, i.e., it defines symmetric non-negative definite kernel. On the next step, optimal classifier is found with the obtained kernel matrix as input. The classifier maximizes the width of hyperplane's separation margin in the space induced by the cluster ensemble kernel. Numerical experiments with artificial examples and real hyperspectral image have shown that the proposed algorithm possesses classification accuracy comparable with some state-of-the-art methods, and in many cases outperforms them, especially in noise conditions.

KW - Cluster ensemble

KW - Co-association matrix

KW - Kernel based learning

KW - Support vector machine

UR - http://www.scopus.com/inward/record.url?scp=85048019912&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:85048019912

VL - 2098

SP - 45

EP - 60

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

SN - 1613-0073

T2 - 2018 School-Seminar on Optimization Problems and their Applications, OPTA-SCL 2018

Y2 - 8 July 2018 through 14 July 2018

ER -