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

  • Adam KiersztynEmail author
  • Paweł Karczmarek
  • Khrystyna Zhadkovska
  • Witold Pedrycz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10842)

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.

Keywords

Expert system Analytic Hierarchy Process (AHP) Decision-making Electricity consumption Biodiversity 

Notes

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).

References

  1. 1.
    Altman, E.I.: Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. J. Finance 23(4), 589–609 (1968)CrossRefGoogle Scholar
  2. 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)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Brown, K.: Integrating conservation and development: a case of institutional misfit. Front. Ecol. Environ. 1(9), 479–487 (2003)CrossRefGoogle Scholar
  4. 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)CrossRefGoogle Scholar
  5. 5.
    Forman, E., Peniwati, K.: Aggregating individual judgments and priorities with the analytic hierarchy process. Eur. J. Oper. Res. 108, 165–169 (1998)CrossRefGoogle Scholar
  6. 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)CrossRefGoogle Scholar
  7. 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)CrossRefGoogle Scholar
  8. 8.
    Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3(Mar), 1157–1182 (2003)zbMATHGoogle Scholar
  9. 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.0000000000000486CrossRefGoogle Scholar
  10. 10.
    Ho, W.: Integrated analytic hierarchy process and its applications-A literature review. Eur. J. Oper. Res. 186, 211–228 (2008)MathSciNetCrossRefGoogle Scholar
  11. 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)CrossRefGoogle Scholar
  12. 12.
    Hooten, M.B., Hobbs, N.T.: A guide to Bayesian model selection for ecologists. Ecol. Monogr. 85(1), 3–28 (2015)CrossRefGoogle Scholar
  13. 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)CrossRefGoogle Scholar
  14. 14.
    Ishizaka, A., Labib, A.: Review of the main developments in the analytic hierarchy process. Expert Syst. Appl. 38, 14336–14345 (2011)CrossRefGoogle Scholar
  15. 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)CrossRefGoogle Scholar
  16. 16.
    Karczmarek, P., Pedrycz, W., Kiersztyn, A.: Graphic interface to analytic hierarchy process and its optimization. IEEE Trans. Fuzzy Syst. (submitted)Google Scholar
  17. 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)CrossRefGoogle Scholar
  18. 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)CrossRefGoogle Scholar
  19. 19.
    van Laarhoven, P.J.M., Pedrycz, W.: A fuzzy extension of Saaty’s priority theory. Fuzzy Sets Syst. 11, 199–227 (1983)MathSciNetCrossRefGoogle Scholar
  20. 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)CrossRefGoogle Scholar
  21. 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)CrossRefGoogle Scholar
  22. 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)CrossRefGoogle Scholar
  23. 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)CrossRefGoogle Scholar
  24. 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)CrossRefGoogle Scholar
  25. 25.
    Pedrycz, W.: Granular Computing. Analysis and Design of Intelligent Systems. CRC Press, Boca Raton (2013)CrossRefGoogle Scholar
  26. 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. 27.
    Saaty, T.L.: Decision-making with the AHP: why is the principal eigenvector necessary. Eur. J. Oper. Res. 145(1), 85–91 (2003)MathSciNetCrossRefGoogle Scholar
  28. 28.
    Saaty, T.L., Vargas, L.G.: Models, Methods, Concepts & Applications of the Analytic Hierarchy Process. Springer, New York (2012).  https://doi.org/10.1007/978-1-4614-3597-6CrossRefzbMATHGoogle Scholar
  29. 29.
    Savard, J.P.L., Clergeau, P., Mennechez, G.: Biodiversity concepts and urban ecosystems. Landsc. Urban Plann. 48(3–4), 131–142 (2000)CrossRefGoogle Scholar
  30. 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)CrossRefGoogle Scholar
  31. 31.
    Sugihara, K., Tanaka, H.: Interval evaluations in the analytic hierarchy process by possibility analysis. Comput. Intell. 17, 567–579 (2001)CrossRefGoogle Scholar
  32. 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)CrossRefGoogle Scholar
  33. 33.
    Vaidya, O.S., Kumar, S.: Analytic hierarchy process: an overview of applications. Eur. J. Oper. Res. 169, 1–29 (2006)MathSciNetCrossRefGoogle Scholar
  34. 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)CrossRefGoogle Scholar
  35. 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)MathSciNetCrossRefGoogle Scholar
  36. 36.
    Zhong, Y.: Analysis of incentive effects of government R&D investment on technology transaction. Mod. Econ. 8, 78–89 (2017)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Adam Kiersztyn
    • 1
    Email author
  • Paweł Karczmarek
    • 1
  • Khrystyna Zhadkovska
    • 1
  • Witold Pedrycz
    • 2
    • 3
    • 4
  1. 1.Institute of Mathematics and Computer ScienceThe John Paul II Catholic University of LublinLublinPoland
  2. 2.Department of Electrical and Computer EngineeringUniversity of AlbertaEdmontonCanada
  3. 3.Department of Electrical and Computer Engineering, Faculty of EngineeringKing Abdulaziz UniversityJeddahSaudi Arabia
  4. 4.Systems Research InstitutePolish Academy of SciencesWarsawPoland

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