DG-ALETSK: A High-Dimensional Fuzzy Approach With Simultaneous Feature Selection and Rule Extraction

Xue, Guangdong; Wang, Jian; Yuan, Bin; Dai, Caili

doi:10.1109/tfuzz.2023.3270445

Cited by 10 publications

(5 citation statements)

References 39 publications

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“…The optimal non-concentric ring images in the geological borehole image recognition database, relative to the original images, reduce the proportion of irrelevant and interfering feature information [37] while preserving effective image feature information. It is beneficial for the classification process.…”

Section: ) Circular Hough Transformmentioning

confidence: 99%

Advancing Borehole Imaging: A Classification Database Developed via Adaptive Ring Segmentation

Deng,

Han,

Liu

et al. 2024

Electronics

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The use of in-hole imaging to investigate geological structure characteristics is one of the crucial methods for the study of rock mass stability and rock engineering design. The in-hole images are usually influenced by the lighting and imaging characteristics, resulting in the presence of interference noise regions in the images and consequently impacting the classification accuracy. To enhance the analytical efficacy of in-hole images, this paper employs the proposed optimal non-concentric ring segmentation method to establish a new database. This method establishes the transformation function based on the Ansel Adams Zone System and the fluctuation values of the grayscale mean, adjusting the gray-level distribution of images to extract two visual blind spots of different scales. Thus, the inner and outer circles are located with these blind spots to achieve the adaptive acquisition of the optimal ring. Finally, we use the optimal non-concentric ring segmentation method to traverse all original images to obtain the borehole image classification database. To validate the effectiveness of this method, we conduct experiments using various segmentation and classification evaluation metrics. The results show that the Jaccard and Dice of the optimal non-concentric ring segmentation approach are 88.43% and 98.55%, respectively, indicating superior segmentation performance compared to other methods. Furthermore, after employing four commonly used classification models to validate the performance of the new classification database, the results demonstrate a significant improvement in accuracy and macro-average compared to the original database, with the highest increase in accuracy reaching 4.2%. These results fully demonstrate the effectiveness of the proposed optimal non-concentric ring segmentation method.

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Section: ) Circular Hough Transformmentioning

confidence: 99%

Advancing Borehole Imaging: A Classification Database Developed via Adaptive Ring Segmentation

Deng,

Han,

Liu

et al. 2024

Electronics

Self Cite

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show abstract

“…For high-dimensional datasets, the number of features is large, especially for the last four datasets, and the feature numbers are all over 20. Considering that fuzzy rules are better at fitting data with small feature dimensions [14], the regression performance of the model is challenged. In addition, in high-dimensional datasets, some of them have small sample sizes, such as FOR and BAS, which increases the risk of overfitting.…”

Section: Datasetsmentioning

confidence: 99%

“…To alleviate "rule explosion", some examples from the literature [14,15] transfer input variables to a new feature space in advance by feature dimension reduction, such as the principal component analysis (PCA), and then carry out the structure and parameter identifications in this new space. For example, [15] restricts the maximum feature dimension to five and utilizes the PCA if the feature number exceeds five.…”

Section: Introductionmentioning

confidence: 99%

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A Symmetrical Fuzzy Neural Network Regression Method Coordinating Structure and Parameter Identifications for Regression

Zhang,

Hao,

et al. 2023

Symmetry

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Fuzzy neural networks have both the interpretability of fuzzy systems and the self-learning ability of neural networks, but they will face the challenge of “rule explosion” when dealing with high-dimensional data. Moreover, the structure and parameter identifications of models are generally performed in two stages, and this always attends to one thing and loses another in terms of interpretability and predictive performance. In this paper, a fuzzy neural network regression method (FNNR) that coordinates structure identification and parameter identification is proposed. To alleviate the problem of rule explosion, the structure identification and parameter identification are coordinated in the training process, and the numbers of fuzzy rules and fuzzy partitions are effectively limited, while the parameters of fuzzy rules are optimized. The symmetrical architecture of the FNNR is designed for automatic structure identification. An alternate training strategy is adopted by treating discrete and continuous parameters differently, and thus the convergence efficiency of the algorithm is improved. To enhance interpretability, regularized terms are designed from fuzzy rule level and fuzzy partition level to guide the model to learn fuzzy rules with simple structures and clear semantics. The experimental results show that the proposed method has both a compact structure and high precision.

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“…In recent years, knowledge distillation has emerged as a powerful technique for improving the performance of student networks in a wide range of tasks and domains, such as object detection [15,16], semantic segmentation [17][18][19], face recognition [20,21] and action recognition [22,23]. Its ability to transfer knowledge from a well-trained teacher network to a smaller student network has made it a popular choice for addressing challenges such as model compression, improving generalization and adapting models to resource-constrained environments.…”

Section: Introductionmentioning

confidence: 99%

Self-Knowledge Distillation via Progressive Associative Learning

Zhao,

Bi,

Tian

et al. 2024

Electronics

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As a specific form of knowledge distillation (KD), self-knowledge distillation enables a student network to progressively distill its own knowledge without relying on a pretrained, complex teacher network; however, recent studies of self-KD have discovered that additional dark knowledge captured by auxiliary architecture or data augmentation could create better soft targets for enhancing the network but at the cost of significantly more computations and/or parameters. Moreover, most existing self-KD methods extract the soft label as a supervisory signal from individual input samples, which overlooks the knowledge of relationships among categories. Inspired by human associative learning, we propose a simple yet effective self-KD method named associative learning for self-distillation (ALSD), which progressively distills richer knowledge regarding the relationships between categories across independent samples. Specifically, in the process of distillation, the propagation of knowledge is weighted based on the intersample relationship between associated samples generated in different minibatches, which are progressively estimated with the current network. In this way, our ALSD framework achieves knowledge ensembling progressively across multiple samples using a single network, resulting in minimal computational and memory overhead compared to existing ensembling methods. Extensive experiments demonstrate that our ALSD method consistently boosts the classification performance of various architectures on multiple datasets. Notably, ALSD pushes forward the self-KD performance to 80.10% on CIFAR-100, which exceeds the standard backpropagation by 4.81%. Furthermore, we observe that the proposed method shows comparable performance with the state-of-the-art knowledge distillation methods without the pretrained teacher network.

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DG-ALETSK: A High-Dimensional Fuzzy Approach With Simultaneous Feature Selection and Rule Extraction

Cited by 10 publications

References 39 publications

Advancing Borehole Imaging: A Classification Database Developed via Adaptive Ring Segmentation

Advancing Borehole Imaging: A Classification Database Developed via Adaptive Ring Segmentation

A Symmetrical Fuzzy Neural Network Regression Method Coordinating Structure and Parameter Identifications for Regression

Self-Knowledge Distillation via Progressive Associative Learning

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