Object

Title: Comparison of Different Dichotomous Classification Algorithms

Abstract:

Experimental investigations of various dichotomous classification algorithms are carried out. Dichotomous classification, or Error-Correcting Output Codes (ECOCs) classification, is based on the construction of a binary code matrix. The rows of the matrix contain unique codewords of classes, and columns are called dichotomies. A dichotomous classification consists of two stages: coding (construction of a code matrix) and decoding, making a decision on the correspondence of an object to a class by analyzing the code matrix. In this study, an experimental comparison of newly proposed methods for constructing dichotomies and a comparison of different approaches to decoding by the available code matrix are proposed. Preliminary experiments show the prospects of proposed methods.

Publisher:

Springer

Date submitted:

17 December 2019

Date accepted:

03 February 2020

Date modified:

03 February 2020

Date of publication:

15 September 2020

Identifier:

oai:noad.sci.am:136094

DOI:

10.1134/S105466182003030X

Journal or Publication Title:

Pattern Recognition and Image Analysis

Volume:

30

Number:

3

URL:


Additional Information:

This study was supported by the Russian Foundation for Basic Research, project nos. 18-51-05011 Arm_a, 17-01-00634, and 18-29-03151.

Affiliation:

Dorodnitsyn Computing Centre, Federal Research Center Computer Science and Control, Russian Academy of Sciences ; Moscow Institute of Physics and Technology (State University) ; Institute for Informatics and Automation Problems, National Academy of Science of Armenia

Year:

2020

References:

Yu. I. Zhuravlev, V. V. Ryazanov, L. H. Aslanyan, and H. A. Sahakyan, “On a classification method for a large number of classes,” Pattern Recogn. Image Anal. 29 (3), 366–376 (2019). ; T. G. Dietterich and G. Bakiri, “Solving multiclass learning problems via error-correcting output codes,” J. Artif. Intell. Res. 2 (1), 263–282 (1995). ; A. P. Bradley, “The use of the area under ROC curve in the evaluation of machine learning algorithms,” Pattern Recogn. 30 (7), 1145-1159 (1997). ; D. M. W. Powers, “Evaluation: From precision, recall, and F-measure to ROC, informedness, markedness, and correlation,” J. Mach. Learn. Technol. 2 (1), 3763 (2011). ; G. H. Ball and D. J. Hall, ISODATA: A Novel Method of Data Analysis and Pattern Classication, Tech. Report NTIS No. AD 699616 (Stanford Research Institute, Menlo Park, 1965). ; A. W. F. Edwards and L. Cavalli-Sforza, “A method for cluster analysis,” Biometrika 56, 362–375 (1965). ; P. McCullagh and J. A. Nelder, Generalized Linear Models, Monographs on Statistics and Applied Probability (Chapman and Hall, CRC Press, London, 1989), Vol. 37. ; M. A. Hearst, “Support Vector Machines,” IEEE Intell. Syst. 13 (4), 18–28 (1998). ; C. L. Blake and C. J. Merz, UCI Repository of Machine Learning Databases (Department of Information and Computer Sciences, University of California, Irvine, 1998). ; F. Pedregosa, G. Varoquaux, A. Gramfort, et al., “Scikit-learn: Machine learning in Python,” J. Mach. Learn. Res. 12, 2825–2830 (2011).

Object collections:

Last modified:

Apr 23, 2021

In our library since:

Apr 16, 2021

Number of object content hits:

7

All available object's versions:

https://noad.sci.am/publication/149753

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