Robust supervised rough granular description model with the principle of justifiable granularity

Ju, Hengrong; Ding, Weiping; Yang, Xibei; Fujita, Hamido; Xu, Suping

doi:10.1016/j.asoc.2021.107612

Cited by 38 publications

(4 citation statements)

References 59 publications

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“…Thus, we introduce the rough membership function into the global evidence support information merging process, which can enhance the local evidence information provided by the majority classes in the neighborhood. The local rough evidence information provided by neighborhood samples with the same class label q is given by [35]:…”

Section: A the Methods Of Fusing The Rough Evidence Informationmentioning

confidence: 99%

“…) Similarly, the global rough evidence information from all samples in the whole neighborhood space M M = = ⊕ can be generated by fusing the local rough evidence information M , and it is obtained as [27], [35]:…”

Section: A the Methods Of Fusing The Rough Evidence Informationmentioning

confidence: 99%

“…Definition 11 ( [27], [35]): The RBel function and RPl function of unclassified sample t x with respect to q X are obtained respectively as:…”

Section: A the Methods Of Fusing The Rough Evidence Informationmentioning

confidence: 99%

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A Novel Spark-Based Attribute Reduction and Neighborhood Classification for Rough Evidence

Ding

Sun

et al. 2024

IEEE Trans. Cybern.

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Neighborhood classification (NEC) algorithms has been widely used to solve classification problems. Most traditional NEC algorithms employ the majority voting mechanism as the basis for final decision-making. However, this mechanism hardly considers the spatial difference and label uncertainty of the neighborhood samples, which may increase the possibility of the misclassification. In addition, the traditional NEC algorithms need to load the whole data into memory at once, which is computationally inefficient when the size of data set is large. To address these problems, we propose a novel Spark-based attribute reduction and neighborhood classification for rough evidence in this paper. Specifically, we first construct a multi-granular sample space using parallel undersampling method. Then, we evaluate the significance of attribute by neighborhood rough evidence decision error rate and remove the redundant attribute on different samples subspaces. Based on this attribute reduction algorithm, we design a parallel attribute reduction algorithm which is able to compute equivalence classes in parallel and parallelize the process of searching for candidate attributes. Finally, we introduce the rough evidence into the classification decision of traditional NEC algorithms and parallelize the classification decision process. Furthermore, the proposed algorithms are conducted in the Spark parallel computing framework. Experimental results on both small and large-scale data sets show that the proposed algorithms outperform the benchmarking algorithms in the classification accuracy and the computational efficiency.

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Section: A the Methods Of Fusing The Rough Evidence Informationmentioning

confidence: 99%

Section: A the Methods Of Fusing The Rough Evidence Informationmentioning

confidence: 99%

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A Novel Spark-Based Attribute Reduction and Neighborhood Classification for Rough Evidence

Ding

Sun

et al. 2024

IEEE Trans. Cybern.

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“…The inherent computational intelligence mechanism is useful for seeking out the optimal solution to complex optimization problems. Different from forward greedy searching, meta-heuristic searching combines both random searching and local searching strategies [21,22]; the global optimal solution can then be gradually achieved in the whole process of searching. However, it should also be emphasized that the random factor in meta-heuristic searching may involve the following limitations.…”

mentioning

confidence: 99%

An Ensemble Framework to Forest Optimization Based Reduct Searching

et al. 2022

Self Cite

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Essentially, the solution to an attribute reduction problem can be viewed as a reduct searching process. Currently, among various searching strategies, meta-heuristic searching has received extensive attention. As a new emerging meta-heuristic approach, the forest optimization algorithm (FOA) is introduced to the problem solving of attribute reduction in this study. To further improve the classification performance of selected attributes in reduct, an ensemble framework is also developed: firstly, multiple reducts are obtained by FOA and data perturbation, and the structure of those multiple reducts is symmetrical, which indicates that no order exists among those reducts; secondly, multiple reducts are used to execute voting classification over testing samples. Finally, comprehensive experiments on over 20 UCI datasets clearly validated the effectiveness of our framework: it is not only beneficial to output reducts with superior classification accuracies and classification stabilities but also suitable for data pre-processing with noise. This improvement work we have performed makes the FOA obtain better benefits in the data processing of life, health, medical and other fields.

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Multi-level granularity entropies for fuzzy coverings and feature subset selection

Huang

2023

Artif Intell Rev

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Robust supervised rough granular description model with the principle of justifiable granularity

Cited by 38 publications

References 59 publications

A Novel Spark-Based Attribute Reduction and Neighborhood Classification for Rough Evidence

A Novel Spark-Based Attribute Reduction and Neighborhood Classification for Rough Evidence

An Ensemble Framework to Forest Optimization Based Reduct Searching

Multi-level granularity entropies for fuzzy coverings and feature subset selection

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