Gaussian kernel fuzzy rough based attribute reduction: An acceleration approach

Rao, Xiansheng; Liu, Keyu; Jin, Zhen; Yang, Xibei; Qian, Yuhua

doi:10.3233/jifs-191633

Cited by 8 publications

(3 citation statements)

References 61 publications

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“…For example, Hu et al [22] adjusted the fuzzy rough approximation with probability, and then proposed a theory about fuzzy probabilistic approximation spaces that develop fuzzy information measures for attribute reduction. Rao et al [23] presented a very quick scheme of feature reduction through taking multiple Gaussian kernels into account in which multiple levels of granularity by different scales of fuzzy granules were considered. Liu et al [24] used fuzzy technique for fuzzy relevance and redundancy to appraise the importance of semi-supervised features in partial decision system also called partially labeled data, and developed a semi-supervised feature selection.…”

Section: Related Workmentioning

confidence: 99%

Effective Attribute Reduction Algorithm Based on Fuzzy Uncertainties Using Shared Neighborhood Granulation

Gao

2024

IEEE Access

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As a very prominent research application of the theory of rough sets, attribute reduction technique has made significant strides in a lot of fields, including decision making, granular computing, etc. In particular, fuzzy attribute reduction approaches contribute greatly in the presence of uncertain data. However, most of fuzzy relations used in these approaches lack the discriminant ability to sample similarity, failing to identify the feature significance satisfactorily. In this article, a novel scheme using the shared neighborhood fuzzy uncertainties is proposed. Firstly, the concept of shared neighborhood is formulated, and then employed to establish the fuzzy similarity relation that effectively captures the sample similarity. Secondly, two fuzzy uncertainty measures named joint entropy and discrimination index based on shared neighborhood fuzzy relation are defined, which can quantify the feature's significance to the uncertainty characterization. Finally, two heuristic searching algorithms are designed to identify reducts aimed at minimizing the fuzzy uncertainties. Some comparative studies are investigated to examine the advantage of the designed reduction algorithms in classifier modeling. The reported analyses on public data sets verify that the designed algorithms outperform some representative and latest algorithms.

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Section: Related Workmentioning

confidence: 99%

Effective Attribute Reduction Algorithm Based on Fuzzy Uncertainties Using Shared Neighborhood Granulation

Gao

2024

IEEE Access

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“…In view of this, various substitutions have been proposed. For instance, fuzzy relation [45,46] induced by kernel function and neighborhood relation [47,48] based on distance function are two widely accepted devices. Both of them are equipped with an advantage of performing information granulation in respect to different scales.…”

Section: Neighborhood Rough Setmentioning

confidence: 99%

Parallel Selector for Feature Reduction

et al. 2023

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In the field of rough set, feature reduction is a hot topic. Up to now, to better guide the explorations of this topic, various devices regarding feature reduction have been developed. Nevertheless, some challenges regarding these devices should not be ignored: (1) the viewpoint provided by a fixed measure is underabundant; (2) the final reduct based on single constraint is sometimes powerless to data perturbation; (3) the efficiency in deriving the final reduct is inferior. In this study, to improve the effectiveness and efficiency of feature reduction algorithms, a novel framework named parallel selector for feature reduction is reported. Firstly, the granularity of raw features is quantitatively characterized. Secondly, based on these granularity values, the raw features are sorted. Thirdly, the reordered features are evaluated again. Finally, following these two evaluations, the reordered features are divided into groups, and the features satisfying given constraints are parallel selected. Our framework can not only guide a relatively stable feature sequencing if data perturbation occurs but can also reduce time consumption for feature reduction. The experimental results over 25 UCI data sets with four different ratios of noisy labels demonstrated the superiority of our framework through a comparison with eight state-of-the-art algorithms.

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“…As a feature selection 1 technology based on fuzzy rough set [2][3][4][5] , attribute reduction [6][7][8] is useful in solving the complex and difficult problems, e.g., the high dimensionality of evaluation indexes in the process of students' evaluation. Such a topic has been concerned by many researchers.…”

Section: Introductionmentioning

confidence: 99%

Research on robust fuzzy rough set to reduce students’ evaluation indexes

Dou

2022

Third International Conference on Computer Science and Communication Technology (ICCSCT 2022)

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Taking the background of student’ evaluation in the college teaching process, to remove irrelevant and redundant indexes which are useless in evaluating the performance of students, this study proposes a quick searching strategy based on fuzzy rough set, and then successfully reduces useless indexes of recording the scores of students. First of all, all indexes can be divided into various groups. Second, only the indexes in all groups that have at least one index in the prospective selected results should be assessed when looking for qualified indexes. It is important to note that this key might quickly decrease the number of potential indexes. Finally, until the constraint is satisfied, both steps are repeated. The success of our study is finally validated using evaluation data from actual students.

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Gaussian kernel fuzzy rough based attribute reduction: An acceleration approach

Cited by 8 publications

References 61 publications

Effective Attribute Reduction Algorithm Based on Fuzzy Uncertainties Using Shared Neighborhood Granulation

Effective Attribute Reduction Algorithm Based on Fuzzy Uncertainties Using Shared Neighborhood Granulation

Parallel Selector for Feature Reduction

Research on robust fuzzy rough set to reduce students’ evaluation indexes

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