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2014, vol. 42, br. 4, str. 25-42
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Primena neuronske mreže na problem kategorizacije ekonomske razvijenosti zemalja
The implementation of the neural networks to the problem of economic classification of countries
aUniverzitet u Kragujevcu, Ekonomski fakultet, Srbija bUniverzitet u Kragujevcu, Fakultet za hotelijerstvo i turizam, Vrnjačka Banja, Srbija
e-adresa: sobradovic@kg.ac.rs
Projekat: Izazovi i perspektive strukturnih promena u Srbiji: strateški pravci ekonomskog razvoja i usklađivanja sa zahtevima EU (MPNTR - 179015)
Ključne reči: ekonomska razvijenost zemalja; indikatori ekonomske razvijenosti; automatska klasifikacija; neuronske mreže; backpropagation algorithm; Matlab neural network toolbox
Keywords: economic development of countries; economic development indicators; neural networks; backpropagation algorithm; Matlab neural network toolbox
Sažetak
Ovaj rad prikazuje praktičnu primenu višeslojne feedforward neuronske mreže, obučavane nadgledano backpropagation algoritmom, na problem automatskog klasifikovanja zemalja u unapred predefinisane kategorije ekonomske razvijenosti, sadržane u izveštaju Ujedinjenih nacija pod nazivom World Economic Situation and Prospects 2012 (WESP 2012). Cilj rada je automatizacija procesa kategorisanja zemalja, definisanje skupa ključnih merljivih indikatora ekonomske razvijenosti, kao i apostrofiranje značaja neuronskih mreža za rešavanje klasifikacionih problema. Istraživanje obuhvata klasifikaciju 168 zemalja u 4 grupe ekonomske razvijenosti upotrebom 7 odabranih merljivih indikatora. Podaci iz zvaničnih izveštaja međunarodnih ekonomskih institucija poslužili su za obučavanje inteligentnog sistema odlučivanja zasnovanog na neuronskoj mreži, a kao mera kvaliteta obuke upotrebljena je confusion matrica, koja prikazuje preciznost inteligentnog sistema utvrđivanjem procenta poklapanja sa iskustveno dobijenim podacima. Preciznost automatske klasifikacije govori o neuronskim mrežama kao moćnom aparatu za rešavanje klasifikacionih problema, ali i o opravdanosti izbora klasifikacionih parametara i njihovoj važnosti. Važnost izabranih indikatora ogleda se u tome što je poznavanje njihovih vrednosti dovoljan uslov za automatsku klasifikaciju nivoa pouzdanosti od 80%.
Abstract
This paper shows practical implementation of the multilayer feedforward neural network, trained by supervised backpropagation algorithm, to the problem of automatic classification of countries into beforehand predefined categories of economic development, contained in the United Nations report entitled World Economic Situation and Prospects 2012. The goal of the paper is to automate the process of classification of countries, to define a set of key measurable economic development indicators, as well as to emphasize significance of neural networks for solving classification problems. The research includes classification of 168 countries in 4 groups of economic development, based on 7 selected measurable indicators. The data from the official reports of the international economic institutions served for training of the intelligent decision-making system based on neural network, and as a measure of quality of training, confusion matrix was used, showing the precision of the intelligent system by determining the percentage of overlap with empirically obtained data. Precision of automatic classification speaks of neural networks as powerful apparatus for solving classification problems, but also of justification of choice of classification parameters and their importance. The importance of selected indicators is reflected in the fact that knowledge of their value is sufficient condition for automatic classification with reliability level of 80%.
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Reference
|
|
|
*** (2011) United Nations Development Programme. Human Development Report 2011 (HDR 2011). http://www.undp.org/content/undp/en/home.html
|
|
|
*** (2013) United Nations Development Programme. Human Development Report 2013 (HDR 2013). http://www.undp.org/content/undp/en/home.html
|
|
|
*** (2012) United Nations. World Economic Situation and Prospects 2012 (WESP 2012). http://www.un.org/en/
|
|
|
Adewole, A.P., Akinwale, A.T., Akintomide, A.B. (2011) Artificial Neural Network Model for Forecasting Foreign Exchange Rate. World of Computer Science and Information Technology Journal (WCSIT), 1(3): 110-, http://v1.wcsit.org/
|
|
|
Al-Shawadfi, A.M.G., Al-Hindi, A.H. (2003) Automatic Classification Using Neural Networks. International Journal of The Computer, The Internet and Management, 11(3): 72-, http://www.ijcim.th.org/
|
|
7
|
Bishop, C.M. (2000) Neural Networks for Pattern Recognition. Oxford university press
|
|
|
Goh, W.L., Mital, D.P., Babri, H.A. (1997) An artificial neural network approach to handwriting recognition. u: Knowledge-Based Intelligent Electronic Systems, First International Conference Adelaide South Australia, http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4869
|
|
|
Haider, A., Hanif, N.M. (2009) Inflation Forecasting in Pakistan Using Artificial Neural Networks. Pakistan Economic and Social Review, 47(1): 123, http://pu.edu.pk/home/journal/7
|
|
|
Hart, A. (1992) Using Neural Networks for Classification Tasks - Some Experiments on Datasets and Practical Advice. Journal of the Operational Research Society, 43(3): 215, http://www.palgrave-journals.com/jors/index.html
|
|
|
Helbicha, M., Hagenauera, J., Leitnerb, M., Edwardsc, R. (2013) Exploration of unstructured narrative crime reports: an unsupervised neural network and point pattern analysis approach. Journal of the Cartography and Geographic Information Society, 40(4): 326, http://www.cartogis.org/
|
|
|
Li, Y. (2013) Evaluation of Psychological Contract Based on Neural Network. International Journal of Applied Mathematics and Statistics, 45(15): 28, http://www.ceser.in/ceserp/index.php/ijamas
|
|
|
Maier, R.H., Dandy, C.G. (2000) Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environmental Modelling & Software, 15(1): 101, http://www.journals.elsevier.com/environmental-modelling-and-software/
|
|
|
Mitchell, R.J., Keating, D.A. (1998) Neural Network Control of Simple Mobile Robot. u: Landau L.J., Taylor J.G. [ur.] Concepts for Neural Networks, London: Springer, : 95-107
|
|
|
Naeini, M.P., Taremian, H., Hashemi, H.B. (2010) Stock Market Value Prediction Using Neural Networks. u: International Conference on Computer Information Systems and Industrial Management Applications (CISIM), http://www.mirlabs.org/cisim10/
|
|
|
Nittis, D.F., Tecchiolli, G., Zorat, A. (1998) Consumer Loan Classification Using Artificial Neural Networks. u: International ICSC Symposium on Engineering of Intelligent Systems, http://www.emeraldinsight.com/journals.htm?articleid=1472160&show=html
|
|
|
Önder, E., Bayɪr, F., Hep, en A. (2013) Forecasting Macroeconomic Variables using Artificial Neural Network and Traditional Smoothing Techniques. Journal of Applied Finance & Banking, 3(4): 73-1, http://www.scienpress.com/journal_focus.asp?main_id=56&Sub_id=IV
|
|
|
Ostafe, D. (2008) Interpretation of ECG Signal with a Multi-Layer Neural Network. Journal of Applied Computer Science, (3): 24-27, http://it.p.lodz.pl/course/view.php?id=12
|
|
|
Prantik, R., Vina, V. (2004) Neural Network Models for Forecasting Mutual Fund Net Asset Value. u: 8th Capital Markets Conference, Indian Institute of Capital Markets Paper, http://papers.ssrn.com/sol3/JELJOUR_Results.cfm?form_name=journalbrowse&journal_id=818444
|
|
|
Ripley, B.D. (1994) Neural Networks and Related Methods for Classification. Journal of the Royal Statistical Society, Series B (Methodological), 56(3): 409, http://search.lib.cam.ac.uk/?itemid=%7Ceresources%7C54729
|
|
|
Russell, J.S., Norvig, P. (2003) Artifical Intelligence - A modern Approach. Pearson Education International
|
|
|
Tagliarini, G.A., Christ, J.F., Page, E.W. (1991) Optimization using neural networks. IEEE Transactions on Computers, 40(12): 1347-1358, http://www.computer.org/portal/web/tc
|
|
|
The World Bank (2013) World Development Indicators, (WDI). http://databank.worldbank.org/data/home.aspx
|
|
|
Tsenov, G.T., Mladenov, V.M. (2010) Speech recognition using neural networks. u: 10th Symposium on Neural Network Applications in Electrical Engineering, http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5628343
|
|
|
Vesely, A. (2011) Economic classification and regression problems and neural networks. Agricultural Economics, 57(3): 150, http://www.agriculturejournals.cz/web/AGRICECON.htm
|
|
|
Yashpal, S., Alok, S.C. (2009) Neural networks in data mining. Journal of Theoretical and Applied Information Technology, 5(1): 37-4, http://www.jatit.org/
|
|
|
Yin, X.Y., Wu, G.C., Yang, F.L. (1996) Predicting oil and gas reservoir and calculating thickness of reservoir from seismic data using neural network. u: 3rd International Conference on Signal Processing Proceedings, http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4274
|
|
|
|
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