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Approach to Judge the Experience of Domain Expert with Uncertainty using Rough Set Theory for Soil Mapping

机译：利用粗糙集理论判断域专家域专家经验的方法

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摘要

In this paper we propose an evolutionary problem solving paradigm to deal with uncertainty of domain expert's experience. Experience of a domain expert can be exploited using Case Based Reasoning (CBR) approach which is stored in the form of Cases in the Case Base. Rough Set Theory is used to deals with uncertainty involve in the experience of domain expert. The main goal of Rough Set Theory is to synthesize approximation of concepts from acquired knowledge which model informative region in the feature space. Hence, hybridization of CBR and Rough Set Theory is efficient technique to obtain the adequate accuracy for soil mapping. The novelty of the proposed work lies in developing of methodology in algorithmic way for knowledge approximation from case base to generate the soil map. The Cases are prepared on the basis of real data collected from different terrain i.e., plain, desert, marshy, costal and deltaic regions. The result is compared with Ground Truth in the field.
机译：在本文中，我们提出了一个解决范例的进化问题，以应对领域专家经验的不确定性。可以使用基于情况的推理（CBR）方法利用域专家的体验，该方法以案例基础的情况形式存储。粗糙集理论用于处理不确定性涉及域专家的经验。粗糙集理论的主要目标是综合来自所获得的知识的概念的近似值，该知识在特征空间中模拟信息区域的模型。因此，CBR和粗糙集理论的杂交是获得土壤映射足够精度的有效技术。所提出的工作的新颖性在于从案例碱基产生土壤图的知识近似的算法方式的方法。该案件是根据从不同地形中收集的真实数据制备的，平原，沙漠，沼泽，昂贵和红细胞区域。结果与地面真相进行了比较。

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