Fuzzy rough granular self-organizing map and fuzzy rough entropy

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

doi:10.1016/j.tcs.2012.08.021

Cited by 26 publications

(10 citation statements)

References 24 publications

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“…Vluymans et al [19] introduced a novel kind of classifier for imbalanced multi-instance data based on fuzzy-rough set theory. Ganivada et al [150] proposed a Fuzzy-Rough Granular Self-Organising Map (FRGSOM) by including the threedimensional linguistic vector and connection weights for clustering the patterns which included overlapping regions. Amiri and Jensen [151] introduced three missing imputation approaches based on the fuzzy-rough nearest neighbors, namely, VQNNI, OWANNI, and FRNNI.…”

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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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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“…In [52,53], the authors proposed a SOM model using fuzzy rough set theory. Similar to their work on neural networks discussed in Section 4.5, the input to the network is provided by constructing fuzzy granules based on the input data.…”

Section: Self-organizing Mapsmentioning

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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“…In particular, it is popular in dealing with the incomplete information, vague concept, and uncertain data [15]. Besides, combined with other data mining algorithm, it can produce more hybrid data mining algorithm [16,17].…”

Section: Rough Set Theorymentioning

confidence: 99%

Knowledge Mining Based on Environmental Simulation Applied to Wind Farm Power Forecasting

Niu

et al. 2013

Mathematical Problems in Engineering

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Considering the inherent variability and uncertainty of wind power generation, in this study, a self-organizing map (SOM) combined with rough set theory clustering technique (RST) is proposed to extract the relative knowledge and to choose the most similar history situation and efficient data for wind power forecasting with numerical weather prediction (NWP). Through integrating the SOM and RST methods to cluster the historical data into several classes, the approach could find the similar days and excavate the hidden rules. According to the data reprocessing, the selected samples will improve the forecast accuracy echo state network (ESN) trained by the class of the forecasting day that is adopted to forecast the wind power output accordingly. The developed methods are applied to a case of power forecasting in a wind farm located in northwest of China with wind power data from April 1, 2008, to May 6, 2009. In order to verify its effectiveness, the performance of the proposed method is compared with the traditional backpropagation neural network (BP). The results demonstrated that knowledge mining led to a promising improvement in the performance for wind farm power forecasting.

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Fuzzy rough granular self-organizing map and fuzzy rough entropy

Cited by 26 publications

References 24 publications

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

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

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

Knowledge Mining Based on Environmental Simulation Applied to Wind Farm Power Forecasting

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