Fuzzy rough sets, and a granular neural network for unsupervised feature selection

Ganivada, Avatharam; Ray, Swati; Pal, Sankar K.

doi:10.1016/j.neunet.2013.07.008

Cited by 41 publications

(12 citation statements)

References 21 publications

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“…Ding, Jia, Chen, and Jin () summarized fuzzy and rough neural networks. Ganivada, Ray, and Pal () proposed a fuzzy rough granular neural networks for feature selection. Song and Pedrycz () proposed a granular network formed on the basis of an existing numeric neural network.…”

Section: Introductionmentioning

confidence: 99%

A GIS‐based multi‐criteria seismic vulnerability assessment using the integration of granular computing rule extraction and artificial neural networks

Sheikhian

Delavar

Stein

2017

Transactions in GIS

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This study proposes multi-criteria group decision-making to address seismic physical vulnerability assessment. Granular computing rule extraction is combined with a feed forward artificial neural network to form a classifier capable of training a neural network on the basis of the rules provided by granular computing. It provides a transparent structure despite the traditional multi-layer neural networks. It also allows the classifier to be applied on a set of rules for each incoming pattern. Drawbacks of original granular computing (GrC) are covered, where some input patterns remained unclassified. The study was applied to classify seismic vulnerability of the statistical units of the city of Tehran, Iran. Slope, seismic intensity, height and age of the buildings were effective parameters. Experts ranked 150 randomly selected sample statistical units with respect to their degree of seismic physical vulnerability. Inconsistency of the experts' judgments was investigated using the induced ordered weighted averaging (IOWA) operator. Fifty-five classification rules were extracted on which a neural network was based. An overall accuracy of 88%, j 5 0.85 and R 2 5 0.89 was achieved. A comparison with previously implemented methodologies proved the proposed method to be the most accurate solution to the seismic physical vulnerability of Tehran.

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Section: Introductionmentioning

confidence: 99%

A GIS‐based multi‐criteria seismic vulnerability assessment using the integration of granular computing rule extraction and artificial neural networks

Sheikhian

Delavar

Stein

2017

Transactions in GIS

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“…In recent decades, rough sets and fuzzy-rough sets theories have been employed in various application areas such as data mining [4,5,86,[301][302][303], software packages [141,304], web ontology [138,[305][306][307], pattern recognition [24,148,187,[308][309][310], granular computing [38,221,238,251], genetic algorithm [310][311][312][313], prototype selection [145,163], solid transportation [146,314,315], social networks [316][317][318], artificial neural network [92,153,319], remote sensing [320,321], and gene selection [158,[322][323][324]]. An et al [140] analysed a regression algorithm based on fuzzy partition, fuzzy-rough sets, estimation of regression values, and fuzzy approximation for estimating wind speed.…”

Section: Distribution Of Papers Based On Other Application Areasmentioning

confidence: 99%

Recent Fuzzy Generalisations of Rough Sets Theory: A Systematic Review and Methodological Critique of the Literature

et al. 2017

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Rough set theory has been used extensively in fields of complexity, cognitive sciences, and artificial intelligence, especially in numerous fields such as expert systems, knowledge discovery, information system, inductive reasoning, intelligent systems, data mining, pattern recognition, decision-making, and machine learning. Rough sets models, which have been recently proposed, are developed applying the different fuzzy generalisations. Currently, there is not a systematic literature review and classification of these new generalisations about rough set models. Therefore, in this review study, the attempt is made to provide a comprehensive systematic review of methodologies and applications of recent generalisations discussed in the area of fuzzy-rough set theory. On this subject, the Web of Science database has been chosen to select the relevant papers. Accordingly, the systematic and metaanalysis approach, which is called "PRISMA," has been proposed and the selected articles were classified based on the author and year of publication, author nationalities, application field, type of study, study category, study contribution, and journal in which the articles have appeared. Based on the results of this review, we found that there are many challenging issues related to the different application area of fuzzy-rough set theory which can motivate future research studies.

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“…An application of the granular neural network for identifying salient features of data, based on the concepts of fuzzy set and a newly defined fuzzy rough set, is proposed in Ganivada et al (2013). In this paper, a three-layered fuzzy rough granular network is then introduced for feature selection, based on the concept of fuzzy sets and the proposed fuzzy rough set.…”

Section: Trends and Applicationsmentioning

confidence: 99%

A study of granular computing in the agenda of growth of artificial neural networks

Song

Wang

2016

Granul. Comput.

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Granular neural networks (GNN) are designed to process complex non-numerical data or the combination of numerical and non-numerical data. The concept of ''granules'' here refers to various data groups which are drawn together by the criteria of similarity or functionality. Granular neural networks are being used in areas of knowledge discovery, pattern recognition, etc. This paper carries out a comprehensive review of articles that involve a comparative study of different types of granular neural networks and their application. This study aims to give useful insight into the capability of granular neural networks.

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Fuzzy rough sets, and a granular neural network for unsupervised feature selection

Cited by 41 publications

References 21 publications

A GIS‐based multi‐criteria seismic vulnerability assessment using the integration of granular computing rule extraction and artificial neural networks

A GIS‐based multi‐criteria seismic vulnerability assessment using the integration of granular computing rule extraction and artificial neural networks

Recent Fuzzy Generalisations of Rough Sets Theory: A Systematic Review and Methodological Critique of the Literature

A study of granular computing in the agenda of growth of artificial neural networks

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