A context-dependent knowledge model for evaluation of regional environment |
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| Affiliation: | 1. Hôpital Jean-Minjoz, pôle mère-femme, CHRU de Besançon, 3, boulevard Fleming, 25000 Besançon, France;2. Laboratoire de biostatistique, faculté de médecine, place Saint-Jacques, 25030 Besançon, France;1. Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia;2. Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia;3. Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China;4. Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China;5. Academy for Advanced Interdisciplinary Studies, Southern University of Science and Technology, Shenzhen 518055, China;1. Center for Engineering Research, Research Institute, King Fahd University of Petroleum and Minerals, Dhahran-31261, Saudi Arabia;2. Institute for Turbulence-Noise-Vibration Interaction and Control, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen 518055, China;3. Key Kab of Advanced Manufacturing and Technology, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China;1. School of Chemistry and Chemical Engineering, Southeast University, Nanjing, 211189, China;2. School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, 325035, China |
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| Abstract: | In this paper we develop a rule-based model for evaluation of regional environment based on both hard and soft data, where by hard data we mean the statistical measurements while by soft data we mean subjective appreciation of human beings of environmental issues. As people's feeling strongly depends on the social and economical characteristics of administrative regions where they live, we firstly use the hard data concerning these characteristics to do clustering in order to obtain clusters corresponding to regions with the homogeneous social and economical characteristics relatively. We then use the soft data, with the help of data-mining techniques, to develop rule-based models which show association between evaluated items of residents in the clusters. Finally, a relationship between hard data and soft data through an integrated model will be explored. It is shown that the soft data are rather reliable and we should integrate subjective knowledge learnt from soft data into modelling of environmental issues. |
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