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 -