Research output: Contribution to journal › Conference article › peer-review
Similarity-based decision tree induction method and its application to cancer recognition on tomographic images. / Berikov, V. B.; Pestunov, I. A.; Kozinets, R. M. et al.
In: Journal of Physics: Conference Series, Vol. 1368, No. 5, 052035, 27.11.2019.Research output: Contribution to journal › Conference article › peer-review
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TY - JOUR
T1 - Similarity-based decision tree induction method and its application to cancer recognition on tomographic images
AU - Berikov, V. B.
AU - Pestunov, I. A.
AU - Kozinets, R. M.
AU - Rylov, S. A.
N1 - Funding Information: The research presented in Section 4 was supported by the Russian Foundation for Basic Research, project 19-29-01175. The study presented in Sections 2,3 was supported by the Russian Foundation for Basic Research, project 18-07-00600, by the Russian Academy of Science (the Program of basic research), project 0314-2019-0015, and by the Russian Ministry of Science and Education under the 5-100 Excellence Programme. Publisher Copyright: © 2019 IOP Publishing Ltd. All rights reserved. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2019/11/27
Y1 - 2019/11/27
N2 - The paper proposes a pattern recognition method using a modification of the class of logical decision functions presented in the form of decision tree. Instead of standard statements corresponding to the tree nodes, in which a variable is tested for a certain set of its values, a more general type of statements is used regarding the similarity of the point in question to different subsets of the observations. At the same time, to determine the degree of similarity, various metrics and subspaces of features can be used. This type of decision tree allows one to obtain more complex decision boundaries, which at the same time have a clear logical interpretation for the user. Several tree induction strategies are considered based on data transformation using support points selected with Relief, SVM, and k -means procedures. The method is experimentally investigated on the problem of tomographic images analysis, as well as on several synthetic datasets. Experiments have shown that the proposed method gives more accurate predictions than CART, SVM, kNN classifiers and deep convolutional neural network (AlexNet).
AB - The paper proposes a pattern recognition method using a modification of the class of logical decision functions presented in the form of decision tree. Instead of standard statements corresponding to the tree nodes, in which a variable is tested for a certain set of its values, a more general type of statements is used regarding the similarity of the point in question to different subsets of the observations. At the same time, to determine the degree of similarity, various metrics and subspaces of features can be used. This type of decision tree allows one to obtain more complex decision boundaries, which at the same time have a clear logical interpretation for the user. Several tree induction strategies are considered based on data transformation using support points selected with Relief, SVM, and k -means procedures. The method is experimentally investigated on the problem of tomographic images analysis, as well as on several synthetic datasets. Experiments have shown that the proposed method gives more accurate predictions than CART, SVM, kNN classifiers and deep convolutional neural network (AlexNet).
UR - http://www.scopus.com/inward/record.url?scp=85077320413&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/1368/5/052035
DO - 10.1088/1742-6596/1368/5/052035
M3 - Conference article
AN - SCOPUS:85077320413
VL - 1368
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
SN - 1742-6588
IS - 5
M1 - 052035
T2 - 5th International Conference on Information Technology and Nanotechnology, ITNT 2019
Y2 - 21 May 2019 through 24 May 2019
ER -
ID: 28277659