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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 1-4/2010

01-12-2010 | Original Article

Full-class set classification using the Hungarian algorithm

Author: Ludmila I. Kuncheva

Published in: International Journal of Machine Learning and Cybernetics | Issue 1-4/2010

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Abstract

Consider a set-classification task where c objects must be labelled simultaneously in c classes, knowing that there is only one object coming from each class (full-class set). Such problems may occur in automatic attendance registration systems, simultaneous tracking of fast moving objects and more. A Bayes-optimal solution to the full-class set classification problem is proposed using a single classifier and the Hungarian assignment algorithm. The advantage of set classification over individually based classification is demonstrated both theoretically and experimentally, using simulated, benchmark and real data.
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Footnotes
1
The relevance of the logarithm will transpire later in relation to the Bayes optimality of the set classifier. The base of the logarithm can be any.
 
2
The Matlab code for the Hungarian algorithm was written by Alex Melin, University of Tennessee, 2006, available through Matlab Central.
 
Literature
1.
Amit Y, Trouvé A (2007) POP: patchwork of parts models for object recognition. Int J Comput Vis 75:267–282CrossRef
2.
3.
Belongie S, Malik J, Puzicha J (2002) Shape matching and object recognition using shape contexts. IEEE Trans Pattern Analy Mach Intell 24:509–522CrossRef
4.
Dietterich TG, Lathrop RH, Lozano-Perez T (1997) Solving the multiple-instance problem with axis-parallel rectangles. Artif Intell 89:31–71MATHCrossRef
5.
Duda RO, Hart PE, Stork DG (2001) Pattern classification, 2nd edn. Wiley, NY
6.
7.
8.
Kaucic R, Perera AGA, Brooksby G, Kaufhold J, Hoogs A (2005) A unified framework for tracking through occlusions and across sensor gaps. In: IEEE computer society conference on computer vision and pattern recognition, CVPR, vol 1, pp 1063–1069
9.
Kuhn HW (1955) The Hungarian method for the assignment problem. Nav Res Logist Q 2:83–97CrossRef
10.
Kuncheva LI (2002) A theoretical study on expert fusion strategies. IEEE Trans Pattern Anal Mach Intell 24(2):281–286CrossRef
11.
Mangasarian OL, Wild EW (2008) Multiple instance classification via successive linear programming. J Optim Theory Appl 137:555–568MATHCrossRefMathSciNet
12.
McDowell LK, Gupta KM, Aha DW (2007) Cautious inference in collective classification. In: Processdings of AAAI, pp 596–601
13.
Ning X, Karypis G (2009) The set classification problem and solution methods. In: Proceedings of SIAM data mining, pp 847–858
14.
Provost F, Domingos P (2003) Tree induction for probability-based ranking. Mach Learn 52(3):199–215MATHCrossRef
15.
Ripley BD (1996) Pattern recognition and neural networks. University Press, CambridgeMATH
16.
Sen P, Namata G, Bilgic M, Getoor L, Gallagher B, Eliassi-Rad T (2008) Collective classification in network data. AI Magazine 29:93–106
17.
Wang J, Zucker J-Dl (2000) Solving the multiple-instance problem: a lazy learning approach. In: Proceedings 17th international conference on machine learning, pp 1119–1125
18.
Zadrozny B, Elkan C (2001) Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers. In: Proceedings of the eighteenth international conference on machine learning (ICML’01), pp 609–616
19.
Zadrozny B, Elkan C (2002) Transforming classifier scores into accurate multiclass probability estimates. In: Proceedings of the 8th international conference on knowledge discovery and data mining (KDD’02)
Metadata
Title
Full-class set classification using the Hungarian algorithm
Author
Ludmila I. Kuncheva
Publication date
01-12-2010
Publisher
Springer-Verlag
Published in
International Journal of Machine Learning and Cybernetics / Issue 1-4/2010
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-010-0002-z

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