QUALITY OF RESEARCH RESULTS IN AGRO-ECONOMY BY DATA MINING

  • Gordana Vukelić, PhD Beogradska bankarska akademija, Fakultet za bankarstvo, osiguranje i fnansije, Belgrade
  • Slobodan Stanojević, PhD Univerzitet privredna akademija u Novom Sadu, Fakultet za mandžment, ekonomiju i fnansije, Belgrade
  • Zorica Anđelić, M.A. VCC Akademija

Abstract

Data Mining (DM) through data in agroeconomy is a scientifc method that enables researchers not to go through set research scenarioes that are predetermined assumptions and hypotheses on the basis of insignifcant atributes. On the contrary, by data mining detection of these atributes is made possible, in general, those hiden facts that enable setting a hypothesis. The DM method does this by an iterative way, including key atributes and factors and their influence on the quality of agro-resources. The research was conducted on a random sample, by analyzing the quality of eggs. The research subject is the posibility of classifying and predicting signifcant variablesatributes that determine the level of egg quality. The research starts from the use of Data Mining, as an area of machine studies, which signifcantly helps researchers in optimizing research. The applied methodology during research includes analyticalsintetic procedures and methods of Data Mining, with a special focus on using Supervised linear discrimination analysis and the Decision Tree. The results indicate significant posibilities of using DM as an additional analytical procedure in performing agroresearch and it can be concluded that it contributes to an improvement in effectiveness and validity of process in performing these researches.

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References

1. Birch, A.N.E., Krogh, P.H., Cortet, J., Tabone, E., Griffths, B.S., Džeroski, S., Wesseler, J., Gomot de Vaufleury, A., Badot, P-M., Andersen, M.N., Messéan, A. (2003): Soil ecological and economic evaluation of genetically modifed crops, Poster at Biodiversity Implications of Genetically, ECOGEN, Vol. 51, pp. 171-173.
2. Bohanec, M., Džeroski, S., Žnidaršić, M. (2003): Multi-attribute modelling of economic and ecological impacts of cropping systems, September 7-12, 2003 Monte Verità, Ascona.
3. Breiman, L. (2001): “Random Forests”, Machine Learning, Vol. 45, No. 1, pp. 5–32.
4. Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J. (1984): Classifcation and Regresssion Trees, Wadsworth, Belmont.
5. Chang, C.C., Lin, C.J. (2001):ACM Transactions on Intelligent Systems and Technology, (TIST LIBSVM) A library for support vector machines, Vol. 2, No. 3, p 27.
6. Cherkassky, V., Mulier, F. M. (2007): Learning from Data: Concepts, Theory, and Methods, 2nd edition, John Wiley - IEEE Press, USA.
7. Demšar, D., Džeroski, S., Krogh, P.H., and Larsen, T. (2003): Modeling microathropods and identifying the most important agricultural factors for the soil community of microathropods, Proceedings of the International Electrotechnical and Computer Science Conference. Ljubljana, Slovenia.
8. Đinović, V. (2013): Uticaj postpupka revalorizacije po fnansijski položaj preduzeća, Oditor, Belgrade, Serbia, No. 4, pp. 14-19.
9. Kantardžić, M. (2002): Data mining: concepts, models, methods, and algorithms’, John Wiley & Sons, Wiley—IEEE Press.
10. Kohavi, R. (1995): “A Study of Cross-validation and Bootstrap for Accuracy Estimation and Model Selection”, in Proc. of International Joint Conference on Artifcial Intelligence, Vol. 14, No. 2, pp. 1137-1145
11. Maindonald, J., Braun, J. (2007): Data Analysis and Graphics Using R, 2nd Edition, Cambridge University Press, Cambridge, ISBN: 9780521762939
12. Mihajlović, M. (2014): Menadžment znanja kao factor povećanja efkasnosti organizacije, Oditor, Belgrade, Serbia, No. 9, pp. 33-36.
13. Platt, J. C. (1998): Sequential minimal optimization: A fast algorithm for training support vector machines, Technical Report MSR-TR-98-8, Microsoft Research.
14. Quinlan, J. R. (1996): “Bagging, Boosting and C4.5”, in Proc. of AAAI-96 Fourteenth national Conference on Artifcial Intelligence, Portland, OR, AAAI Press, Menlo Park, CA, Vol. 1, pp. 725-730.
15. Sadok, W., Angevin, F., Bergez, J. É., Bockstaller, C., Colomb, B., Guichard, L., Reau, R., Doré, T. (2009). Ex ante Assessment of the Sustainability of Alternative Cropping Systems: Implications for Using Multi-criteria Decision-Aid Methods-A Review. In Sustainable Agriculture, pp. 753-767, Springer Netherlands.
16. Sohl, J. E., Venkatachalam, A. R. (1995): A neural network approach to forecasting model selection, Information and Management, Vol. 29, No. 6, pp. 297-303.
17. Stanojević, S. (2013): Мultivarijaciona analiza fnansijskih izveštaja, doktorska disertacija, Beogradska bankarska kademija, Fakultet za bankarstvo, fnansije I osiguranje, Univerzitet UNION, Beograd.
18. Witten, I. H., Frank, E., Hall, M. A. (2011): Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann Publishers.
Published
2015-12-31
How to Cite
VUKELIĆ, Gordana; STANOJEVIĆ, Slobodan; ANĐELIĆ, Zorica. QUALITY OF RESEARCH RESULTS IN AGRO-ECONOMY BY DATA MINING. Economics of Agriculture, [S.l.], v. 62, n. 4, p. 1137-1146, dec. 2015. ISSN 2334-8453. Available at: <http://ea.bg.ac.rs/index.php/EA/article/view/268>. Date accessed: 22 oct. 2020. doi: https://doi.org/10.5937/ekoPolj1504137V.