A fuzzy rough set approach for incremental feature selection on hybrid information systems

Zeng, An‐Ping; Li, Tianrui; Liu, Dun; Chen, Hongmei

doi:10.1016/j.fss.2014.08.014

Cited by 185 publications

(41 citation statements)

References 50 publications

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“…Kuznetsov et al [251] introduced an approach based on the fuzzy-rough sets which were called the ( , )-fuzzyrough set. Zeng et al [52] developed a new Hybrid Distance (HD) in Hybrid Information System (HIS) based on the value difference metric, and a new fuzzy-rough approach was designed by integrating the Gaussian kernel and HD distance. Feng and Mi [39] investigated variable precision multigranulation fuzzy decision-theoretic rough sets in an information system.…”

Section: Distribution Papers Based On Information Systemsmentioning

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 Information Systemsmentioning

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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“…A Zeng P et al [30] proposed a feature incremental learning method based on Fuzzy Rough Set (FRS). The mixed information system's data (HIS) often changed over time, and the data was varied (such as: real, Boolean and set values etc).…”

Section: Feature Incremental Learningmentioning

confidence: 99%

A Survey on Incremental Learning

Zhong¹,

Liu²,

Zeng³

et al. 2017

2017 5th International Conference on Computer, Automation and Power Electronics (CAPE 2017)

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Abstract:Incremental learning is one of the research hotspots in machine learning. In this paper, we view the complex changes of data as three changes that are the change of sample, the change of class and the change of feature, and analyze the popular machine learning classification algorithms which support incremental learning. And then we focus on reviewing the research of three types of incremental learning: Sample Incremental Learning, Class Incremental Learning and Feature Incremental Learning. Finally, we make a prospect on the focus and difficulty of future research of incremental learning.

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“…Ghosh et al proposed an efficient Gaussian kernel-based fuzzy rough sets approach for feature selection [10]. A novel fuzzy rough sets model was constructed by combining the hybrid distance and the Gaussian kernel in [11]. A new feature selection method based on kernel fuzzy rough sets and a memetic algorithm were proposed for the transient stability assessment of power systems [12].…”

Section: Introductionmentioning

confidence: 99%

Kernel Neighborhood Rough Sets Model and Its Application

Zeng

Jing

2018

Complexity

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Rough set theory has been successfully applied to many fields, such as data mining, pattern recognition, and machine learning. Kernel rough sets and neighborhood rough sets are two important models that differ in terms of granulation. The kernel rough sets model, which has fuzziness, is susceptible to noise in the decision system. The neighborhood rough sets model can handle noisy data well but cannot describe the fuzziness of the samples. In this study, we define a novel model called kernel neighborhood rough sets, which integrates the advantages of the neighborhood and kernel models. Moreover, the model is used in the problem of feature selection. The proposed method is tested on the UCI datasets. The results show that our model outperforms classic models.

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A fuzzy rough set approach for incremental feature selection on hybrid information systems

Cited by 185 publications

References 50 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

A Survey on Incremental Learning

Kernel Neighborhood Rough Sets Model and Its Application

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