Rough sets, their extensions and applications

Shen, Qiang; Jensen, Richard

doi:10.1007/s11633-007-0217-y

Cited by 60 publications

(19 citation statements)

References 73 publications

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“…Rough set theory provides the function-attribute reduction-to refine the features in a decision system. In this study, an attribute-dependence measurement, denoted by γ, was used to select the most useful attributes for classification purposes [49]. In a fuzzy rough classification system, land cover classes D depending on spectral signature attributes C is defined by…”

Section: Attribute Reductionmentioning

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: Attribute Reductionmentioning

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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“…Two versions of DE based FS methods are presented in [70], where the desired feature subset size can be predefined by the user. There are extensively several works carried out with the help of rough set theory (RST) [53] and its extensions [56]. To overcome few drawbacks of RST, fuzzy set theory has been hybridized with it in [50,51].…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

Survey on Feature Selection for Data Reduction

Bania¹

2014

IJCA

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The storage capabilities and advanced in data collection has led to an information load and the size of databases increases in dimensions, not only in rows but also in columns. Data reduction (DR) plays a vital role as a data prepossessing techniques in the area of knowledge discovery from the huge collection of data. Feature selection (FS) is one of the well known data reduction techniques, which deals with the reduction of attributes from the original data without affecting the main information content. Based on the training data used for different applications of knowledge discovery, FS technique falls into supervised, unsupervised. In this paper an extensive survey on supervised FS technique describing the different searching approach, methods and application areas with an outline of a comparative study is covered.

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“…On the other hand, the generalization of rough sets is an interesting topic not only in mathematical point of view but also in practical point of view. Along this direction, rough sets have been generalized under similarity relations (Inuiguchi and Tanino 2001), covers (Bonikowski et al 1998) and general relations (Inuiguchi and Tanino 2002;Shen and Jensen 2007;Yao 1996Yao , 1998Yao and Lin 1996). Those results demonstrate a diversity of generalizations.…”

Section: Introductionmentioning

confidence: 99%

Generalized multigranulation rough sets and optimal granularity selection

Zhang

2017

Granul. Comput.

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Rough sets, their extensions and applications

Cited by 60 publications

References 73 publications

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

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

Survey on Feature Selection for Data Reduction

Generalized multigranulation rough sets and optimal granularity selection

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