A~quantitative measure of compactness and similarity in competitive space
Sibirskij žurnal industrialʹnoj matematiki, Tome 13 (2010) no. 1, pp. 59-71.

Voir la notice de l'article provenant de la source Math-Net.Ru

We describe similarity measures among objects in metric and competitive spaces. We propose a competitive similarity function as a similarity measure used in classification and pattern recognition problems. This function enables us to construct some efficient algorithms for solving all main data mining problems, to obtain quantitative estimates for the compactness of images and the informativeness of trait spaces, and to construct easily interpretable decision rules. The method applies to problems with arbitrary numbers of images and characters of their distributions, and can also be used for solving poorly conditioned problems.
Keywords: similarity measure, pattern recognition, compactness, informativeness.
@article{SJIM_2010_13_1_a5,
     author = {N. G. Zagoruǐko and I. A. Borisova and V. V. Dyubanov and O. A. Kutnenko},
     title = {A~quantitative measure of compactness and similarity in competitive space},
     journal = {Sibirskij \v{z}urnal industrialʹnoj matematiki},
     pages = {59--71},
     publisher = {mathdoc},
     volume = {13},
     number = {1},
     year = {2010},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/SJIM_2010_13_1_a5/}
}
TY  - JOUR
AU  - N. G. Zagoruǐko
AU  - I. A. Borisova
AU  - V. V. Dyubanov
AU  - O. A. Kutnenko
TI  - A~quantitative measure of compactness and similarity in competitive space
JO  - Sibirskij žurnal industrialʹnoj matematiki
PY  - 2010
SP  - 59
EP  - 71
VL  - 13
IS  - 1
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/item/SJIM_2010_13_1_a5/
LA  - ru
ID  - SJIM_2010_13_1_a5
ER  - 
%0 Journal Article
%A N. G. Zagoruǐko
%A I. A. Borisova
%A V. V. Dyubanov
%A O. A. Kutnenko
%T A~quantitative measure of compactness and similarity in competitive space
%J Sibirskij žurnal industrialʹnoj matematiki
%D 2010
%P 59-71
%V 13
%N 1
%I mathdoc
%U http://geodesic.mathdoc.fr/item/SJIM_2010_13_1_a5/
%G ru
%F SJIM_2010_13_1_a5
N. G. Zagoruǐko; I. A. Borisova; V. V. Dyubanov; O. A. Kutnenko. A~quantitative measure of compactness and similarity in competitive space. Sibirskij žurnal industrialʹnoj matematiki, Tome 13 (2010) no. 1, pp. 59-71. http://geodesic.mathdoc.fr/item/SJIM_2010_13_1_a5/

[1] Voronin Yu. A., Nachala teorii skhodstva, izd. VTs SO RAN, Novosibirsk, 1989

[2] Shreider Yu. A., Ravenstvo, skhodstvo, poryadok, Nauka, M., 1971 | MR

[3] Fix E., Hodges J., Discriminatory Analysis. Nonparametric Discrimination. Consistency Properties, Technical report. Rep. 21-49-004, USAF School of AviationMed., Randolph Field, TX, 1951

[4] Kira K., Rendell L., “The Feature selection problem: Traditional methods and a new algorithm”, Proc. 10 Conf. “Artificial Intelligence”, AAAI-92, AAAI Press, Menlo Park, 1992, 129–134

[5] Zagoruiko N. G., Prikladnye metody analiza dannykh i znanii, Izd. VTs SO RAN, Novosibirsk, 1999 | Zbl

[6] Braverman E. M., “Experiences on training the machine to recognition of visual patterns”, Automatics and Telemechanics, 23:3 (1962), 349–365

[7] Vorontsov K. V., Koloskov A. O., “Profili kompaktnosti i vydelenie opornykh ob'ektov v metricheskikh algoritmakh klassifikatsii”, Iskusstvennyi intellekt, 2006, no. 2, 30–33

[8] Zagoruiko N. G., Borisova I. A., Dyubanov V. V., Kutnenko O. A., “Methods of recognition based on the function of rival similarity”, Pattern Recognition and Image Analysis, 18:1 (2008), 1–6

[9] Guyon I., Weston J., Barnhill S., Vapnik V., “Gene selection for cancer classification using support vector machines”, Machine Learning, 46:1–3 (2002), 389–422 | DOI | Zbl

[10] Golub T. R., Slonim D. K., Tamayo P., Huard C., Gaasenbeek M., Mesirov J. P., Coller H., Loh M. L., Downing J. R., Caligiuri M. A., Bloomfield C. D., Lander E. S., “Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring”, Science, 286 (1999), 531–537 ; www.genome.wi.mit.edu/MPR/data_set_ALL_AML.html | DOI

[11] Merill T., Green O. M., “On the effectiveness of receptions in recognition systems”, IEEE Trans. Inform. Theory, 9:1 (1963), 11–17 | DOI

[12] Borisova I. A., Dyubanov V. V., Zagoruiko N. G., Kutnenko O. A., “Skhodstvo i kompaktnost”, Trudy 14 Vseros. konf. “Matematicheskie metody raspoznavaniya obrazov”, M., 2009, 89–92

[13] Zagoruiko N. G., Kutnenko O. A., Ptitsin A. A., “Algorithm GRAD for selection of informative genetic feature”, Proc. Internat. Conf. on Computational Molecular Biology, Moscow, 2005, 8–9

[14] Zagoruiko N. G., Kutnenko O. A., “Recognition methods based on the AdDel algorithm”, Pattern Recognition and Image Analysis, 14:2 (2004), 198–204

[15] Dyubanov V. V., “Primenenie FRiS-funktsii dlya resheniya zadachi prognozirovaniya sprosa (algoritm FRiS-Pro)”, Tr. Vseros. konf. “Znaniya-Ontologii-Teorii”, ZONT-09, T. 2, Novosibirsk, 2009, 258–260

[16] Bogdanov A. B., Borisova I. A., Dyubanov V. V., Zagoruiko N. G., Kutnenko O. A., Kuchkin A. V., Mescheryakov M. A., Milovzorov N. G., “Intellektualnyi analiz spektralnykh dannykh”, Avtometriya, 2009, no. 1, 92–101