A Novel Fuzzy Rough Granular Neural Network for Classification

Ganivada, Avatharam; Pal, Sankar K.

doi:10.2991/ijcis.2011.4.5.27

Cited by 5 publications

(5 citation statements)

References 18 publications

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“…Mitra and Banerjee () studied granulation of input and output in a neural network and considered granulated neurons for dimensionality reduction and information encoding. Ganivada and Pal () implemented a fuzzy rough granular neural network based on a multilayer perceptron using a back‐propagation algorithm. Ding, Jia, Chen, and Jin () summarized fuzzy and rough neural networks.…”

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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“…These subsets correspond to the output of subnetworks, which are trained by dividing the input data in smaller subgroups. In [50,51,54] a fuzzy rough three-layer fully-connected perceptron using back-propagation to train the connection weights was proposed. The input is fed to the network in granular form, that is, three fuzzy granules low, medium and high are defined for each attribute and the membership degree of all instances to each of these fuzzy sets are determined.…”

Section: Neural Networkmentioning

confidence: 99%

“…Each output node corresponds to a class, to which the membership degree is determined. The authors considered the application of their proposal for both classification [50,51] and feature selection [54].…”

Section: Neural Networkmentioning

confidence: 99%

Applications of Fuzzy Rough Set Theory in Machine Learning: a Survey

Vluymans

D'eer

Saeys

et al. 2015

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Data used in machine learning applications is prone to contain both vague and incomplete information. Many authors have proposed to use fuzzy rough set theory in the development of new techniques tackling these characteristics. Fuzzy sets deal with vague data, while rough sets allow to model incomplete information. As such, the hybrid setting of the two paradigms is an ideal candidate tool to confront the separate challenges. In this paper, we present a thorough review on the use of fuzzy rough sets in machine learning applications. We recall their integration in preprocessing methods and consider learning algorithms in the supervised, unsupervised and semi-supervised domains and outline future challenges. Throughout the paper, we highlight the interaction between theoretical advances on fuzzy rough sets and practical machine learning tools that take advantage of them.

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“…The recent two decades have witnessed the booming interest and growing development in research of RST and its applications. As a kind of information processing tools, RST has been widely applied to the fields of artificial intelligence and cognitive sciences, such as pattern recognition [3], knowledge discovery [4,5], decision making [6,7], inductive reasoning [8] and machine learning [9]. In multiplecriteria decision making problems, attributes with preference-order domain (Criteria) constitute an important kind of attribute and should be brought to our great attention.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Knowledge Update Using Three-Way Decisions in Dominance-Based Rough Sets Approach While the Object Set Varies

Wang¹,

Li²,

Ye³

et al. 2019

IJCIS

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Dominance-based rough set approach is the extension of classical Pawlak rough set theories and methodologies, in which the information with preference-ordered relation on the domain of attribute value is fully considered. In the dominance-based information system, upper and lower approximations will be changed while the object set varies over time and the approximations need to be updated correspondingly for their variations result in changes of knowledge and rules. Considering that three-way decisions is a special class of general and effective human ways of problem solving and information processing, a new incremental maintenance mechanism using three-way decisions is proposed in this paper, namely, the universe is divided into three pair-wise disjoint subsets firstly, then appropriate strategies are developed and acted on each subsets. Furthermore, the corresponding methods for updating the approximations of upward unions and downward unions of decision classes are analyzed systematically under the variations of object set in the dominance-based information system from the perspective of three-way decisions. Moreover, considering vector representation and calculation is intuitive and concise, two incremental update algorithms of approximations are suggested and implemented in the MATLAB platform. Finally, some tests on data sets from UCI (UC Irvine Machine Learning Repository) are undertaken to verify the effectiveness of the proposed methods. Compared with the non-incremental updating methods, the proposed incremental updating method with three-way decisions generally exhibits a better performance.

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A Novel Fuzzy Rough Granular Neural Network for Classification

Cited by 5 publications

References 18 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

Applications of Fuzzy Rough Set Theory in Machine Learning: a Survey

Dynamic Knowledge Update Using Three-Way Decisions in Dominance-Based Rough Sets Approach While the Object Set Varies

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