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Determination of a Matrix of the Dependencies Between Features Based on the Expert Knowledge

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Artificial Intelligence and Soft Computing (ICAISC 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10842))

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Abstract

In the paper, we investigate the problem of replacing long-lasting and expensive research by expert knowledge. The proposed innovative method is a far-reaching improvement of the AHP method. Through the use of a slider, the proposed approach can be used by experts who have not yet met the AHP method or do not feel comfortable when using classic approach related to words and numbers. In the series of experiments, we confirm the efficiency of the method in a modeling of electricity consumption in teleinformatics and in an application of biodiversity to urban planning.

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References

  1. Altman, E.I.: Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. J. Finance 23(4), 589–609 (1968)

    Article  Google Scholar 

  2. Bogdan, M., Van Den Berg, E., Sabatti, C., Su, W., Cands, E.J.: SLOPEadaptive variable selection via convex optimization. Ann. Appl. Stat. 9(3), 1103–1140 (2015)

    Article  MathSciNet  Google Scholar 

  3. Brown, K.: Integrating conservation and development: a case of institutional misfit. Front. Ecol. Environ. 1(9), 479–487 (2003)

    Article  Google Scholar 

  4. Cohen, S.G., Ledford Jr., G.E., Spreitzer, G.M.: A predictive model of self-managing work team effectiveness. Hum. Relat. 49(5), 643–676 (1996)

    Article  Google Scholar 

  5. Forman, E., Peniwati, K.: Aggregating individual judgments and priorities with the analytic hierarchy process. Eur. J. Oper. Res. 108, 165–169 (1998)

    Article  Google Scholar 

  6. Geijzendorffer, I.R., Regan, E.C., Pereira, H.M., Brotons, L., et al.: Bridging the gap between biodiversity data and policy reporting needs: an Essential Biodiversity Variables perspective. J. Appl. Ecol. 53(5), 1341–1350 (2016)

    Article  Google Scholar 

  7. Gungor, V.C., Hancke, G.P.: Industrial wireless sensor networks: challenges, design principles, and technical approaches. IEEE Trans. Ind. Electron. 56(10), 4258–4265 (2009)

    Article  Google Scholar 

  8. Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3(Mar), 1157–1182 (2003)

    MATH  Google Scholar 

  9. Hewett, T.E., Webster, K.E., Hurd, W.J.: Systematic selection of key logistic regression variables for risk prediction analyses: a five-factor maximum model. Clin. J. Sport Med.: off. J. Can. Acad. Sport Med. (2017). https://doi.org/10.1097/JSM.0000000000000486

    Article  Google Scholar 

  10. Ho, W.: Integrated analytic hierarchy process and its applications-A literature review. Eur. J. Oper. Res. 186, 211–228 (2008)

    Article  MathSciNet  Google Scholar 

  11. Holmberg, K., Kivikyt-Reponen, P., Hrkisaari, P., Valtonen, K., Erdemir, A.: Global energy consumption due to friction and wear in the mining industry. Tribol. Int. 115, 116–139 (2017)

    Article  Google Scholar 

  12. Hooten, M.B., Hobbs, N.T.: A guide to Bayesian model selection for ecologists. Ecol. Monogr. 85(1), 3–28 (2015)

    Article  Google Scholar 

  13. Hoyle, H., Hitchmough, J., Jorgensen, A.: All about the wow factor? The relationships between aesthetics, restorative effect and perceived biodiversity in designed urban planting. Landsc. Urban Plann. 164, 109–123 (2017)

    Article  Google Scholar 

  14. Ishizaka, A., Labib, A.: Review of the main developments in the analytic hierarchy process. Expert Syst. Appl. 38, 14336–14345 (2011)

    Article  Google Scholar 

  15. Karczmarek, P., Pedrycz, W., Kiersztyn, A., Rutka, P.: A study in facial features saliency in face recognition: an analytic hierarchy process approach. Soft Comput. 21(24), 7503–7517 (2017)

    Article  Google Scholar 

  16. Karczmarek, P., Pedrycz, W., Kiersztyn, A.: Graphic interface to analytic hierarchy process and its optimization. IEEE Trans. Fuzzy Syst. (submitted)

    Google Scholar 

  17. Khorana, A.A., Kuderer, N.M., Culakova, E., Lyman, G.H., Francis, C.W.: Development and validation of a predictive model for chemotherapy-associated thrombosis. Blood 111(10), 4902–4907 (2008)

    Article  Google Scholar 

  18. Kuo, B.C., Ho, H.H., Li, C.H., Hung, C.C., Taur, J.S.: A kernel-based feature selection method for SVM with RBF kernel for hyperspectral image classification. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 7(1), 317–326 (2014)

    Article  Google Scholar 

  19. van Laarhoven, P.J.M., Pedrycz, W.: A fuzzy extension of Saaty’s priority theory. Fuzzy Sets Syst. 11, 199–227 (1983)

    Article  MathSciNet  Google Scholar 

  20. Lange, C., Kosiankowski, D., Weidmann, R., Gladisch, A.: Energy consumption of telecommunication networks and related improvement options. IEEE J. Sel. Top. Quantum Electron. 17(2), 285–295 (2011)

    Article  Google Scholar 

  21. Łopucki, R., Kiersztyn, A.: Urban green space conservation and management based on biodiversity of terrestrial faunaa decision support tool. Urban For. Urban Green. 14(3), 508–518 (2015)

    Article  Google Scholar 

  22. Mac Nally, R.: Regression and model-building in conservation biology, biogeography and ecology: the distinction between – and reconciliation of – ‘predictive’ and ‘explanatory’ models. Biodivers. Conserv. 9(5), 655–671 (2000)

    Article  Google Scholar 

  23. Palensky, P., Dietrich, D.: Demand side management: demand response, intelligent energy systems, and smart loads. IEEE Trans. Ind. Inform. 7(3), 381–388 (2011)

    Article  Google Scholar 

  24. Pedrycz, W., Song, M.: Analytic hierarchy process (AHP) in group decision making and its optimization with an allocation of information granularity. IEEE Trans. Fuzzy Syst. 19, 527–539 (2011)

    Article  Google Scholar 

  25. Pedrycz, W.: Granular Computing. Analysis and Design of Intelligent Systems. CRC Press, Boca Raton (2013)

    Book  Google Scholar 

  26. Saaty, T.L., Mariano, R.S.: Rationing energy to industries: priorities and input-output dependence. Energy Syst. Policy 3, 85–111 (1979)

    Google Scholar 

  27. Saaty, T.L.: Decision-making with the AHP: why is the principal eigenvector necessary. Eur. J. Oper. Res. 145(1), 85–91 (2003)

    Article  MathSciNet  Google Scholar 

  28. Saaty, T.L., Vargas, L.G.: Models, Methods, Concepts & Applications of the Analytic Hierarchy Process. Springer, New York (2012). https://doi.org/10.1007/978-1-4614-3597-6

    Book  MATH  Google Scholar 

  29. Savard, J.P.L., Clergeau, P., Mennechez, G.: Biodiversity concepts and urban ecosystems. Landsc. Urban Plann. 48(3–4), 131–142 (2000)

    Article  Google Scholar 

  30. Standish, R.J., Hobbs, R.J., Miller, J.R.: Improving city life: options for ecological restoration in urban landscapes and how these might influence interactions between people and nature. Landsc. Ecol. 28(6), 1213–1221 (2013)

    Article  Google Scholar 

  31. Sugihara, K., Tanaka, H.: Interval evaluations in the analytic hierarchy process by possibility analysis. Comput. Intell. 17, 567–579 (2001)

    Article  Google Scholar 

  32. Threlfall, C.G., Mata, L., Mackie, J.A., Hahs, A.K., Stork, N.E., Williams, N.S., Livesley, S.J.: Increasing biodiversity in urban green spaces through simple vegetation interventions. J. Appl. Ecol. 54(6), 1874–1883 (2017)

    Article  Google Scholar 

  33. Vaidya, O.S., Kumar, S.: Analytic hierarchy process: an overview of applications. Eur. J. Oper. Res. 169, 1–29 (2006)

    Article  MathSciNet  Google Scholar 

  34. Yu, D., Xun, B., Shi, P., Shao, H., Liu, Y.: Ecological restoration planning based on connectivity in an urban area. Ecol. Eng. 46, 24–33 (2012)

    Article  Google Scholar 

  35. Yuan, M., Lin, Y.: Model selection and estimation in regression with grouped variables. J. R. Stat. Soc.: Ser. B (Stat. Methodol.) 68(1), 49–67 (2006)

    Article  MathSciNet  Google Scholar 

  36. Zhong, Y.: Analysis of incentive effects of government R&D investment on technology transaction. Mod. Econ. 8, 78–89 (2017)

    Article  Google Scholar 

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Acknowledgements

The authors are supported by National Science Centre, Poland [grant no. 2014/13/D/ST6/03244]. Support from the Canada Research Chair (CRC) program and Natural Sciences and Engineering Research Council is gratefully acknowledged (W. Pedrycz).

Author information

Authors and Affiliations

  1. Institute of Mathematics and Computer Science, The John Paul II Catholic University of Lublin, ul. Konstantynów 1H, 20-708, Lublin, Poland

    Adam Kiersztyn, Paweł Karczmarek & Khrystyna Zhadkovska

  2. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, T6R 2V4, Canada

    Witold Pedrycz

  3. Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

    Witold Pedrycz

  4. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Witold Pedrycz

Authors
  1. Adam Kiersztyn
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  2. Paweł Karczmarek
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  3. Khrystyna Zhadkovska
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  4. Witold Pedrycz
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Corresponding author

Correspondence to Adam Kiersztyn .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Kiersztyn, A., Karczmarek, P., Zhadkovska, K., Pedrycz, W. (2018). Determination of a Matrix of the Dependencies Between Features Based on the Expert Knowledge. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2018. Lecture Notes in Computer Science(), vol 10842. Springer, Cham. https://doi.org/10.1007/978-3-319-91262-2_50

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  • DOI: https://doi.org/10.1007/978-3-319-91262-2_50

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-91261-5

  • Online ISBN: 978-3-319-91262-2

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