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

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.

[1]  Yufeng Zhong.  Analysis of Incentive Effects of Government R & D Investment on Technology Transaction , 2017 .

[2]  Witold Pedrycz,et al.  Granular Computing: Analysis and Design of Intelligent Systems , 2013 .

[3]  Alessio Ishizaka,et al.  Review of the main developments in the analytic hierarchy process , 2011, Expert Syst. Appl..

[4]  Philippe Clergeau,et al.  Biodiversity concepts and urban ecosystems , 2000 .

[5]  C Lange,et al.  Energy Consumption of Telecommunication Networks and Related Improvement Options , 2011, IEEE Journal of Selected Topics in Quantum Electronics.

[6]  Bor-Chen Kuo,et al.  A Kernel-Based Feature Selection Method for SVM With RBF Kernel for Hyperspectral Image Classification , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[7]  Mevin B. Hooten,et al.  A guide to Bayesian model selection for ecologists , 2015 .

[8]  Thomas L. Saaty,et al.  Decision-making with the AHP: Why is the principal eigenvector necessary , 2003, Eur. J. Oper. Res..

[9]  Sushil Kumar,et al.  Analytic hierarchy process: An overview of applications , 2006, Eur. J. Oper. Res..

[10]  Hideo Tanaka,et al.  Interval Evaluations in the Analytic Hierarchy Process By Possibility Analysis , 2001, Comput. Intell..

[11]  Nicholas S. G. Williams,et al.  Increasing biodiversity in urban green spaces through simple vegetation interventions , 2017 .

[12]  W. Pedrycz,et al.  A fuzzy extension of Saaty's priority theory , 1983 .

[13]  Gary H Lyman,et al.  Development and validation of a predictive model for chemotherapy-associated thrombosis. , 2008, Blood.

[14]  Deyong Yu,et al.  Ecological restoration planning based on connectivity in an urban area , 2012 .

[15]  R. M. Nally.  Regression and model-building in conservation biology, biogeography and ecology: The distinction between – and reconciliation of – ‘predictive’ and ‘explanatory’ models , 2000, Biodiversity & Conservation.

[16]  M. Yuan,et al.  Model selection and estimation in regression with grouped variables , 2006 .

[17]  James Hitchmough,et al.  All about the ‘wow factor’? The relationships between aesthetics, restorative effect and perceived biodiversity in designed urban planting , 2017 .

[18]  Adam Kiersztyn,et al.  Urban green space conservation and management based on biodiversity of terrestrial fauna – A decision support tool , 2015 .

[19]  Thomas L. Saaty,et al.  Rationing Energy to Industries: Priorities and Input-Output Dependence , 1982 .

[20]  Timothy E. Hewett,et al.  Systematic Selection of Key Logistic Regression Variables for Risk Prediction Analyses: A Five-Factor Maximum Model , 2017, Clinical journal of sport medicine : official journal of the Canadian Academy of Sport Medicine.

[21]  Gerald E. Ledford,et al.  A Predictive Model of Self-Managing Work Team Effectiveness , 1996 .

[22]  Edward I. Altman,et al.  FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY , 1968 .

[23]  Alessio Ishizaka,et al.  Selection of new production facilities with the Group Analytic Hierarchy Process Ordering method , 2011, Expert Syst. Appl..

[24]  Kirti Peniwati,et al.  Aggregating individual judgments and priorities with the analytic hierarchy process , 1998, Eur. J. Oper. Res..

[25]  Richard J. Hobbs,et al.  Improving city life: options for ecological restoration in urban landscapes and how these might influence interactions between people and nature , 2012, Landscape Ecology.

[26]  K. Brown.  Integrating conservation and development: a case of institutional misfit , 2003 .

[27]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[28]  Peter Haase,et al.  Bridging the gap between biodiversity data and policy reporting needs: An Essential Biodiversity Variables perspective , 2016 .

[29]  Weijie J. Su,et al.  SLOPE-ADAPTIVE VARIABLE SELECTION VIA CONVEX OPTIMIZATION. , 2014, The annals of applied statistics.

[30]  Peter Palensky,et al.  Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads , 2011, IEEE Transactions on Industrial Informatics.

[31]  Kenneth Holmberg,et al.  Global energy consumption due to friction and wear in the mining industry , 2017 .

[32]  William Ho,et al.  Integrated analytic hierarchy process and its applications - A literature review , 2008, Eur. J. Oper. Res..

[33]  L. Zadeh.  The role of fuzzy logic in the management of uncertainty in expert systems , 1983 .

[34]  Gerhard P. Hancke,et al.  Industrial Wireless Sensor Networks: Challenges, Design Principles, and Technical Approaches , 2009, IEEE Transactions on Industrial Electronics.

[35]  Witold Pedrycz,et al.  Analytic Hierarchy Process (AHP) in Group Decision Making and its Optimization With an Allocation of Information Granularity , 2011, IEEE Transactions on Fuzzy Systems.

[36]  Witold Pedrycz,et al.  A study in facial features saliency in face recognition: an analytic hierarchy process approach , 2016, Soft Computing.