Nižje tveganje pri upravljanju kmetijstva za vlado z uporabo regresije podpornih vektorjev s parametri dinamične optimizacije
DOI:
https://doi.org/10.4335/15.2.243-261(2017)Ključne besede:
oblikovanje kmetijske politike, upravljanje kmetijstva, regresijski model podpornih vektorjev, metoda vzorčenja, analiza masovnih podatkovPovzetek
Dobra kmetijska politika lahko zmanjša tveganje pri upravljanju kmetijstva. V preteklosti so se kot pomoč pri upravljanju kmetijstva vedno uporabljale tradicionalne statistične metode. Vendar pa predpostavke o tradicionalnih metodah morda ne ustrezajo realnim podatkom, ki bi vplivali na odločitve o upravljanju kmetijstva. Iz tega razloga je v tem prispevku uporabljena analiza masovnih podatkov, s katero se predlaga nov napovedni model brez kakršne koli predpostavke o napovedovanju kmetijske proizvodnje za znižanje tveganja. Glede na rezultat je v smislu natančnosti napovedovanja predlagani model boljši kot obstoječi modeli. Skladno s tem se lahko predlagani model priporoči za zmanjševanje tveganja pri vladnem upravljanju kmetijstva.
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Xia, Z., Zhi, Z., Tong, S., Kun, S. & Yanli, S. (2014) Improved GM (1, 1) model for sea level change prediction, 2014 IEEE Geoscience and Remote Sensing Symposium, pp. 4469-4472.
Xiong, T., Li, C., Bao, Y., Hu, Z. & Zhang, L. (2015) A combination method for interval forecasting of agricultural commodity futures prices, Knowledge-Based Systems, 77, pp. 92-102.
Yousefi, M., Khoshnevisan, B., Shamshirband, S., Motamedi, S., Nasir, M. H. N., Arif, M., & Ahmad, R. (2015) Support vector regression methodology for prediction of output energy in rice production, Stochastic Environmental Research and Risk Assessment, 29(8), pp. 2115-2126.
Zheng, M-C. & Chou, J-H. (2011) A novel nonlinear forecasting model for output of bike industry by Grey model and Taguchi-differential evolution algorithm, African Journal of Business Management, 5(12), pp. 4945-4954.
Zięba, M. & Tomczak, J. M. (2014) Boosted SVM with active learning strategy for imbalanced data, Soft Computing, 19(12), pp. 3357-3368.
Ao, Z., Ou, X-Q. & Zhu, S. (2011) A New Optimization Approach for Grey Model GM(1,1), In: Lin, S. & Huang X (eds) Advanced Research on Computer Education, Simulation and Modeling, vol 175. Communications in Computer and Information Science (Berlin, Heidelberg: Springer), pp 117-122.
Boser, B. E., Guyon, I. M. & Vapnik, V. N. (1992) A training algorithm for optimal margin classifiers. Paper presented at the Proceedings of the fifth annual workshop on Computational learning theory, Pittsburgh, Pennsylvania, USA.
Cervantes, J., Li, X., Yu, W. & Li, K. (2008) Support vector machine classification for large data sets via minimum enclosing ball clustering, Neurocomputing, 71, pp. 611-619.
Chang, T-Y. (2011) The influence of agricultural policies on agriculture structure adjustment in Taiwan: An analysis of off-farm labor movement, China Agricultural Economic Review, 3(1), pp. 67-79
Coshall, J. T. (2009) Combining volatility and smoothing forecasts of UK demand for international tourism, Tourism Management, 30(4), pp. 495-511.
DeLurgio, S.A. (1998) Forecasting Principles and Applications. 1 edn. (New York: Irwin/McGraw-Hill).
Drucker, H., Burges, C. J. C., Kaufman, L., Smola, A. J. & Vapnik, V. (1996) Support Vector Regression Machines. Paper presented at the NIPS'1996.
Huang, S-C. (2008) Online option price forecasting by using unscented Kalman filters and support vector machines, Expert Systems with Applications, 34(4), pp. 2819-2825.
Huang, M. (2015) Agricultural Economic Evaluation Based on Improved Support Vector Regression, 2015 8th International Conference on Intelligent Computation Technology and Automation (ICICTA), pp. 118-121.
Hsu, L-C. (2011) Using improved grey forecasting models to forecast the output of opto-electronics industry, Expert Systems with Applications, 38(11), pp. 13879-13885.
Kordos, M. & Rusiecki, A. (2015) Reducing noise impact on MLP training, Soft Computing, 20(1), pp. 49-65.
Lavanya, K., Raguchander, T. & Iyengar, N. C. S. (2013) SVM Regression and SONN based approach for seasonal crop price prediction, International Journal of u-and e-Service, Science and Technology, 6, pp. 155-168.
Lee, C-P. & Leu, Y. (2011) A novel hybrid feature selection method for microarray data analysis, Applied Soft Computing, 11(1), pp. 208-213.
Lee, C-P., Lin, W-C. & Yang, C-C. (2014) A strategy for forecasting option price using fuzzy time series and least square support vector regression with a bootstrap model, Scientia Iranica, 21, pp. 815-825.
Lee, C-P., Shieh, G-J., Yiu, T-J. & Kuo, B-J. (2013) The strategy to simulate the cross-pollination rate for the co-existence of genetically modified (GM) and non-GM crops by using FPSOSVR, Chemometrics and Intelligent Laboratory Systems, 122, pp. 50-57.
Lewis, J. (2014) Bayesian Restricted Likelihood Methods. (Electronic Thesis or Dissertation), available at: https://etd.ohiolink.edu/ (December 12, 2016).
Li, B., Zheng, D., Sun, L. & Yang, S. (2007) Exploiting multi-scale support vector regression for image compression, Neurocomputing, 70(16-18), pp. 3068-3074.
Liang, X., Zhang, H., Xiao, J. & Chen, Y. (2009) Improving option price forecasts with neural networks and support vector regressions, Neurocomputing, 72(13-15), pp. 3055-3065.
Liao, D. G. & Luo, Y. X. (2011) Grey new information GOM(1,1) model and its application based on opposite-direction accumulated generating and background value optimization, Advanced Materials Research, 321, pp. 33-36.
Lin, H-C. & Kao, T-M. (2006) The economic impact from agricultural products loss caused by natural disasters and regional input-output analysis in Taiwa, Taiwanese Agricultural Economic Review, 12, pp.105-138.
Meza, F. J., Hansen, J. W. & Osgood, D. (2008) Economic value of seasonal climate forecasts for agriculture: review of ex-ante assessments and recommendations for future research, Journal of Applied Meteorology and Climatology, 47, pp. 1269-1286.
Nieh, S.-C., Lin, W.-S., Hsu, Y.-H., Shieh, G.-J. & Kuo, B.-J. (2014) The effect of flowering time and distance between pollen source and recipient on Maize, GM Crops & Food: Biotechnology in Agriculture and the Food Chain, 5(4), pp. 21-33.
Ou, S-L. (2012) Forecasting agricultural output with an improved grey forecasting model based on the genetic algorithm, Computers and Electronics in Agriculture, 85, pp. 33-39.
Singh, S. (2009) Global food crisis: magnitude, causes and policy measures, International Journal of Social Economics, 36(1/2), pp. 23-36.
Slabe-Erker, R., Klun, M. & Lampič, B. (2016) Assessment of agricultural sustainability at regional level in Slovenia, Lex Localis-Journal of Local Self-Government, 14(2), pp. 209-223.
Viala, E. (2008) Water for food, water for life a comprehensive assessment of water management in agriculture, Irrigation and Drainage Systems, 22(1), pp. 127-129.
Wu, W-Y., Hsiao, S-W. & Tsai, C-H. (2008) Forecasting and Evaluating the Tourist Hotel Industry Performance in Taiwan Based on Grey Theory, Tourism and Hospitality Research, 8(2), pp. 137-152.
Xia, Z., Zhi, Z., Tong, S., Kun, S. & Yanli, S. (2014) Improved GM (1, 1) model for sea level change prediction, 2014 IEEE Geoscience and Remote Sensing Symposium, pp. 4469-4472.
Xiong, T., Li, C., Bao, Y., Hu, Z. & Zhang, L. (2015) A combination method for interval forecasting of agricultural commodity futures prices, Knowledge-Based Systems, 77, pp. 92-102.
Yousefi, M., Khoshnevisan, B., Shamshirband, S., Motamedi, S., Nasir, M. H. N., Arif, M., & Ahmad, R. (2015) Support vector regression methodology for prediction of output energy in rice production, Stochastic Environmental Research and Risk Assessment, 29(8), pp. 2115-2126.
Zheng, M-C. & Chou, J-H. (2011) A novel nonlinear forecasting model for output of bike industry by Grey model and Taguchi-differential evolution algorithm, African Journal of Business Management, 5(12), pp. 4945-4954.
Zięba, M. & Tomczak, J. M. (2014) Boosted SVM with active learning strategy for imbalanced data, Soft Computing, 19(12), pp. 3357-3368.
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Nižje tveganje pri upravljanju kmetijstva za vlado z uporabo regresije podpornih vektorjev s parametri dinamične optimizacije. (2017). Lex Localis - Journal of Local Self-Government, 15(2), 243-261. https://doi.org/10.4335/15.2.243-261(2017)
