Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Pattern Recognition and Image Analysis
Published in: Pattern Recognition and Image Analysis 3/2019

01-07-2019 | MATHEMATICAL METHOD IN PATTERN RECOGNITION

On a Classification Method for a Large Number of Classes

Authors: Yu. I. Zhuravlev, V. V. Ryazanov, L. H. Aslanyan, H. A. Sahakyan

Published in: Pattern Recognition and Image Analysis | Issue 3/2019

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

The construction of a two-level decision scheme for recognition problems with many classes is proposed that is based on the development of the error-correcting output codes (ЕСОС) method. In the “classical” ЕСОС, a large number of partitions of the original classes into two macroclasses are constructed. Each macroclass is a union of some original classes. Each macroclass is assigned either 0 or 1. As a result, each original class is defined by a row of 0 and 1 (the stage of encoding) and a coding matrix is constructed. The stage of classification of an arbitrary new object consists in the solution of each dichotomic problem and application of a special decision rule (the stage of decoding). In this paper, new methods for weighting and taking into account codewords, modifying decision rules, and searching for locally optimal dichotomies are proposed, and various quality criteria for classification and the cases of extension of a codeword are considered.
previous article Lightweight Nearest Convex Hull Classifier
next article Adaptive Detection of Normal Mixture Signals with Pre-Estimated Gaussian Mixture Noise

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now
Literature
1.
C. M. Bishop, Pattern Recognition and Machine Learning (Springer, New York, 2006).MATH
2.
T. G. Dietterich and G. Bakiri, “Solving multiclass learning problems via error-correcting output codes,” J. Artif. Intell. Res. 2 (1), 263–286 (1995).CrossRefMATH
3.
E. L. Allwein, R. E. Schapire, and Y. Singer, “Reducing multiclass to binary: A unifying approach for margin classifiers,” J. Mach. Learn. Res. 1, 113–141 (2000).MathSciNetMATH
4.
S. Escalera, O. Pujol, and P. Radeva, “Separability of ternary codes for sparse designs of error-correcting output codes,” Pattern Recogn. Lett. 30 (3), 285–297 (2009).CrossRef
5.
S. Escalera, O. Pujol, and P. Radeva, “Error-correcting ouput codes library,” J. Mach. Learn. Res. 11, 661–664 (2010).
6.
M. Sahare and H. Gupta, “A review of multi-class classification for imbalanced data,” Int. J. Adv. Comput. Res. 2 (5), 160–164 (2012).
7.
M. R.Gupta, S. Bengio, and J. Weston, “Training highly multiclass classifiers,” J. Mach. Learn. Res. 15, 1461–1492 (2014).MathSciNetMATH
8.
W. Bi and J. T. Kwok, “Efficient multi-label classification with many labels,” in Proc. 30th Int. Conf. on Machine Learning (ICML−2013) (Atlanta, GA, USA, 2013), pp. III-405–III-413.
9.
T. M. Cover and P. E. Hart, “Nearest neighbor pattern classification,” IEEE Trans. Inf. Theory 13 (1), 21–27 (1967).CrossRefMATH
10.
J. R. Quinlan, “Induction of decision trees,” Mach. Learn. 1 (1), 81–106 (1986).
11.
Yu. I. Zhuravlev and V. V. Nikiforov, “Recognition algorithms based on the computation of estimates,” Kibernetika, No. 3, 1–11 (1971) [in Russian].
12.
P. McCullagh and J. A. Nelder, Generalized Linear Models, in Monographs on Statistics and Applied Probability (Chapman and Hall / CRC Press, London, 1989), Vol. 37.
13.
R. A. Fisher, “The use of multiple measurements in taxonomic problems,” Ann. Eugen. 7 (2), 179–188 (1936).CrossRef
14.
M. A. Hearst, “Support Vector Machines,” IEEE Intell. Syst. 13 (4), 18–28 (1998).CrossRef
15.
16.
L. I. Kuncheva and C. J. Whitaker, “Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy,” Mach. Learn. 51 (2), 181–207 (2003).CrossRefMATH
17.
L. I. Kuncheva, “Using diversity measures for generating errorcorrecting output codes in classifier ensembles,” Pattern Recogn. Lett. 26 (1), 83–90 (2005).CrossRef
18.
O. Pujol, P. Radeva, et al., “Discriminant ECOC: A heuristic method for application dependent design of error correcting output codes,” IEEE Trans. Pattern Anal. Mach. Intell. 28 (6), 1007–1012 (2006).CrossRef
19.
K. Crammer and Y. Singer, “On the learnability and design of output codes for multiclass problems,” Mach. Learn. 47 (2), 201–233 (2002).CrossRefMATH
20.
Yu. I. Zhuravlev and V. V. Ryazanov, “Solution of instance-based recognition problems with a large number of classes,” Dokl. Math. 96 (2), 488–490 (2017).MathSciNetCrossRefMATH
21.
A. P. Bradley, “The use of the area under the ROC curve in the evaluation of machine learning algorithms,” Pattern Recogn. 30 (7), 1145–1159 (1997).CrossRef
22.
D. M. W. Powers, “Evaluation: From Precision, Recall, and F-measure to ROC, Informedness, Markedness, and Correlation,” J. Mach. Learn. Technol. 2 (1), 37–63 (2011).
23.
X. L. Zhang, “Heuristic ternary error-correcting output codes via weight optimization and layered clustering-based approach,” IEEE Trans. Cybern. 45 (2), 289–301 (2015).CrossRef
24.
B. Desgraupes, Clustering Indices (University Paris Ouest Lab Modal’X, 2013). https://​cran.​r-project.​org/​web/​packages/​clusterCrit/​index.​html
25.
F. B. Baker and L. J. Hubert, “Measuring the power of hierarchical cluster analysis,” J. Am. Stat. Assoc. 70 (349), 31–38 (1975).CrossRefMATH
26.
G. H. Ball and D. J. Hall, Isodata: A Novel Method of Data Analysis and Pattern Classification, Technical Report NTIS No. AD 699616 (Stanford Research Institute, Menlo Park, CA, 1965).
27.
A. W. F. Edwards and L. L. Cavalli-Sforza, “A method for cluster analysis,” Biometrics 21 (2), 362–375 (1965).CrossRef
28.
K. M. Ting, Confusion Matrix, in Encyclopedia of Machine Learning, Ed. by C. Sammut and G. I. Webb (Springer, Boston, MA, 2011).
29.
J. T. Townsend, “Theoretical analysis of an alphabetic confusion matrix,” Perception Psychophys. 9 (1), 40–50 (1971).CrossRef
30.
S. A. Vavasis, “Quadratic programming is in NP Stephen,” Inf. Process. Lett. 36 (2), 73–77 (1990).CrossRefMATH
Metadata
Title
On a Classification Method for a Large Number of Classes
Authors
Yu. I. Zhuravlev
V. V. Ryazanov
L. H. Aslanyan
H. A. Sahakyan
Publication date
01-07-2019
Publisher
Pleiades Publishing
Published in
Pattern Recognition and Image Analysis / Issue 3/2019
Print ISSN: 1054-6618
Electronic ISSN: 1555-6212
DOI
https://doi.org/10.1134/S1054661819030246

Other articles of this Issue 3/2019

Pattern Recognition and Image Analysis 3/2019 Go to the issue

REPRESENTATION, PROCESSING, ANALYSIS, AND UNDERSTANDING OF IMAGES

Panoramic Image Mosaics via Distributed Systems Using Color Moments and Local Wavelet-Features

APPLIED PROBLEMS

Vision Based Approach for Adaptive Parking Lots Occupancy Estimation

GENERAL PROBLEMS, METHODOLOGY AND PRINCIPLES OF PATTERN RECOGNITION AND IMAGE ANALYSIS

Mathematical Foundations for Processing High Data Volume, Machine Learning, and Artificial Intelligence

APPLIED PROBLEMS

Application of Quantum-Clustering on Thermograms of WiFi Circuits in Different Operation Modes

REPRESENTATION, PROCESSING, ANALYSIS, AND UNDERSTANDING OF IMAGES

Image Classification Model Using Visual Bag of Semantic Words

APPLIED PROBLEMS

Detection and Removal of Foreground Objects in Spherical Images for the Synthesis of Photorealistic Intermediate Images

Premium Partner

    Image Credits