Fuzzy-rough feature selection accelerator

Qian, Yuhua; Wang, Qi; Cheng, Hao; Liang, Jiye; Dang, Chuangyin

doi:10.1016/j.fss.2014.04.029

Cited by 133 publications

(31 citation statements)

References 46 publications

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“…Jensen et al [83] proposed a model by using a rough set for solving the problems related to the propositional satisfiability perspective. Qian et al [84] proposed an approach based on dimensionality reduction together with sample reduction for a heuristic process of fuzzy-rough feature selection. Derrac et al [88] presented a new hybrid algorithm for reduction of data using feature and instance selection.…”

Section: Distribution Papers Based On Feature or Attribute Selectionmentioning

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 Papers Based On Feature or Attribute Selectionmentioning

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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“…Memetic feature selection algorithm for multi-label classification is developed to prevent premature convergence and gives better accuracy [40]. Fuzzy-rough [41] feature selection based on forward approximation is developed and used for feature selection on high dimensional dataset [42]. Fast Fourier Transform (FFT [43]) based feature selection algorithm is developed for mechanical system.…”

Section: Literature Surveymentioning

confidence: 99%

A Survey on Feature Selection Algorithms

Kumar¹

2015

IJRITCC

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Abstract-One major component of machine learning is feature analysis which comprises of mainly two processes: feature selection and fe ature extraction. Due to its applications in several areas including data mining, soft computing and big data analysis, feature selection has got a reasonable importance. This paper presents an introductory concept of feature selection with various inherent approaches. The paper surveys historic developments reported in feature selection with supervised and unsupervised methods. The recent developments with the state of the art in the on-going feature selection algorithms have also been summarized in the paper including their hybridizations.

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“…Rough set theory, proposed by Pawlak [34,35] has been conceived as an excellent tool to analyze and handle intelligent systems characterized by imprecise, vague and uncertain information in many fields, such as data mining, knowledge discovery, decision making and so on [5,6,8,9,12,19,21,24,26,37,38,43,44,49,50,54,57] .…”

Section: Introductionmentioning

confidence: 99%