Managing uncertainty in location services using rough set and evidence theory

Sikder, Iftikhar U.; Gangopadhyay, Aryya

doi:10.1016/j.eswa.2005.12.015

Cited by 39 publications

(16 citation statements)

References 43 publications

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“…Several studies have focused on the relationship between rough set theory and the DS-based evidential reasoning [44,45]. These studies show that belief (plausibility) functions can be derived from a classic Pawlak rough set.…”

Section: Interpretation Of Evidential Reasoning On Fuzzy Rough Setmentioning

confidence: 99%

Classification of Complex Urban Fringe Land Cover Using Evidential Reasoning Based on Fuzzy Rough Set: A Case Study of Wuhan City

Yang

et al. 2016

Remote Sensing

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Urban fringe is the transition zone fine grained with urban and non-urban land cover types. The complex landscape mosaic in this area challenges the land cover classification based on the remote-sensing data. Spectral signatures are not efficient to discriminate all pixels into classes. To improve the recognition and handle the uncertainty, this paper provides a novel integrated approach, based on a fuzzy rough set and evidential reasoning (FRSER), for land cover classification in an urban fringe area. The approach is implemented on Landsat Operation Land Imager data covering the urban fringe area of Wuhan city, China. A fuzzy rough set is first used to define a decision table from multispectral imagery and ground reference data. Then the fuzzy rough information system is interpreted using the Dempster-Shafer theory, based on an evidential reasoning system. A final land cover classification with uncertainty is achieved by evidential reasoning. The results are compared with the traditional maximum likelihood classifier (MLC) and some rough set-based classifiers including classical rough set classifier (RS), fuzzy rough set classifier (FRS), and variable precision fuzzy rough set classifier (VPFRS). The better overall accuracy, user's and producer's accuracies, and the kappa coefficient, in comparison with the other classifiers, suggest that the proposed approach can effectively discriminate land cover types in urban fringe areas with high inter-class similarities and intra-class heterogeneity. It is also capable of handling the uncertainty in data processing, and the final land cover map comes with a degree of uncertainty. The proposed approach that can efficiently integrate the merits of both the fuzzy rough set and DS theory provides an efficient method for urban fringe land cover classification.

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Section: Interpretation Of Evidential Reasoning On Fuzzy Rough Setmentioning

confidence: 99%

Classification of Complex Urban Fringe Land Cover Using Evidential Reasoning Based on Fuzzy Rough Set: A Case Study of Wuhan City

Yang

et al. 2016

Remote Sensing

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“…RST (Pawlak 1991) is a useful mathematical tool for identifying hidden knowledge, characterized by vague or uncertain information, which can then be classified without deterioration of the information, in order to generate decision rules. RST has been successfully applied to problems of vague and uncertain information in a wide range of fields, and has provided many exciting results, such as image segmentation (Mushrif and Ray 2008), learning models for lowlevel medical data (Brtka et al 2008), location services (Sikder and Gangopadhyay 2007), analysis of customer complaints , financing (Sanchis et al 2007), travel modeling (Witlox and Tindemans 2004), and medicine (Tsumoto 2000). However, one of the problems that RST faces is an attribute reduction issue.…”

Section: Introductionmentioning

confidence: 98%

A Rough Set Based Model in Water Quality Analysis

Pai

Lee

2009

Water Resour Manage

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Due to pollution caused by the expansion of human activities and economic development, water quality has gradually deteriorated in many areas of the world. Therefore, analysis of water quality becomes one of the most essential issues of modern civilization. Integrated interdisciplinary modeling techniques, providing reliable, efficient, and accurate representation of the complex phenomenon of water quality, have gained attention in recent years. With the ability to deal with both numeric and nominal information, and express knowledge in a rule-based form, the Rough Set Theory (RST) has been successfully employed in many fields. However, the application of RST has not been widely investigated in water quality analysis. The reducts generated by RST models become very time-consuming as the size of the problem increases. Using multinomial logistics regression (MLR) techniques to provide reducts of RST models, this investigation develops a hybrid Multinomial Logistic Regression and Rough Set Theory (MLRRST) model to analyze relations between degrees of water pollution and environmental factors in Taiwan. Empirical results indicate that the MLRRST model could analyze water qualities efficiently and accurately, and yield decision rules for the staff of water quality management. Thus, the proposed model is a promising and helpful scheme in analyzing water quality.

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“…RST has been successfully applied to problems with vagueness and uncertainty of information, and it has provided many exciting results in a considerably wide range of fields, such as medicine (Tsumoto, 2000), travel modeling (Witlox & Tindemans, 2004), location services (Sikder & Gangopadhyay, 2007), analysis of customer complaints (Yang, Liu, & Lin, 2007), financing (Sanchis, Segovia, Gil, Heras, & Vilar, 2007), learning models of low-level medical data (Brtka, Stotić , & Srdić , 2008), and image segmentation (Mushrif & Ray, 2008). SVM (Vapnik & Cortes, 1995) is a classification technique and has better generalization ability than that of traditional approaches.…”

Section: Introductionmentioning

confidence: 99%