A Novel Variable Precision Reduction Approach to Comprehensive Knowledge Systems

Yang, Chuanfang; Liu, Hongbo; McLoone, Seán; Chen, C. L. Philip; Wu, Xindong

doi:10.1109/tcyb.2017.2648824

Cited by 11 publications

(2 citation statements)

References 48 publications

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“…In recent years, the research on rough set theory in the field of artificial intelligence has been a hot topic, and this theory has been widely used. Knowledge reduction [23][24][25] is one of the core concepts of rough set theory.…”

Section: Rough Set Neural Networkmentioning

confidence: 99%

Rough Set Neural Network Feature Extraction and Pattern Recognition of Shaft Orbits Based on the Zernike Moment

Zhang

Zhou

et al. 2021

Shock and Vibration

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In the shaft axis monitoring of hydrogenerating unit condition monitoring and fault diagnosis, the shaft orbit is intuitive and comprehensively reflects the unit operation state, and different shaft orbits correspond to different fault types, which can accurately indicate a system vibration fault. Shaft orbit identification has important significance for vibration fault diagnosis. In getting the feature extraction and pattern recognition of a shaft orbit, the Zernike moment is better than the Hu moment; it has the advantages of a small calculation error and a high recognition rate. A rough set neural network (RS-BP hybrid model) of shaft orbit recognition is established, which uses just 13 moment eigenvalues reserved by the rough set feature selection algorithm as input variables; it has the same calculation error and recognition rate and reduces the calculation time step. The simulation of the recognition of shaft orbits shows that the hybrid model has achieved good results in the identification of shaft orbits.

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Section: Rough Set Neural Networkmentioning

confidence: 99%

Rough Set Neural Network Feature Extraction and Pattern Recognition of Shaft Orbits Based on the Zernike Moment

Zhang

Zhou

et al. 2021

Shock and Vibration

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“…Multi-reductions provide more insights from different perspectives than a single reduction and can form a multi-knowledge system [22]. Unfortunately, it is a major challenge to obtain multi-reduction [23]. Wu et al [24] proposed a method to obtain multi-reduction based on positive region by replacing the non-core attributes.…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Multi-Reduction Algorithm for Brain Functional Connection Pathways Analysis

et al. 2019

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Revealing brain functional connection pathways is of great significance in understanding the cognitive mechanism of the brain. In this paper, we present a novel rough set based dynamic multi-reduction algorithm (DMRA) to analyze brain functional connection pathways. First, a binary discernibility matrix is introduced to obtain a reduction, and a reduction equivalence theorem is proposed and proved to verify the feasibility of reduction algorithm. Based on this idea, we propose a dynamic single-reduction algorithm (DSRA) to obtain a seed reduction, in which two dynamical acceleration mechanisms are presented to reduce the size of the binary discernibility matrix dynamically. Then, the dynamic multi-reduction algorithm is proposed, and multi-reductions can be obtained by replacing the non-core attributes in seed reduction. Comparative performance experiments were carried out on the UCI datasets to illustrate the superiority of DMRA in execution time and classification accuracy. A memory cognitive experiment was designed and three brain functional connection pathways were successfully obtained from brain functional Magnetic Resonance Imaging (fMRI) by employing the proposed DMRA. The theoretical and empirical results both illustrate the potentials of DMRA for brain functional connection pathways analysis.

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A Novel Rough Sets Positive Region Based Parallel Multi-reduction Algorithm

Dai

Jiang

et al. 2018

Advances in Intelligent Systems and Computing

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A Novel Variable Precision Reduction Approach to Comprehensive Knowledge Systems

Cited by 11 publications

References 48 publications

Rough Set Neural Network Feature Extraction and Pattern Recognition of Shaft Orbits Based on the Zernike Moment

Rough Set Neural Network Feature Extraction and Pattern Recognition of Shaft Orbits Based on the Zernike Moment

A Dynamic Multi-Reduction Algorithm for Brain Functional Connection Pathways Analysis

A Novel Rough Sets Positive Region Based Parallel Multi-reduction Algorithm

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