Double-quantitative distance measurement and classification learning based on the tri-level granular structure of neighborhood system

Zhang, Xianyong; Gou, Hongyuan; Lv, Zhiyong; Miao, Duoqian

doi:10.1016/j.knosys.2021.106799

Cited by 50 publications

(4 citation statements)

References 62 publications

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“…: IF is in the neighborhood of , THEN should be classified as risk group (15) where is the candidate data point; is the selected prototype;…”

Section: E Information Granules and Risk Measuresmentioning

confidence: 99%

“…In addition, the granular computation does not require tunning many hyperparameters of the machine-learning models and thus can avoid overfitting to the local optimal solution. The granular computation has been successfully applied in many typical data-driven problems such as classification [12][13][14][15], regression [16], time-series prediction [17][18] and anomaly detection [19]. Especially inspired by the work of Ouyang et al [14], the utilization of granular computation in landslide risk assessment has been adopted in this research.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Landslide Risk Assessment Using Granular Fuzzy Rule-Based Modeling: A Case Study on Earthquake-Triggered Landslides

Shi

Zeng²,

Tang

et al. 2021

IEEE Access

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Landslides are one type of destructive and recurring natural calamities in the mountainous regions. The landslide occurrences often lead to immense damage to local infrastructure and loss of land, human lives and livestocks. Data-driven risk assessment of landslide risk plays a crucial role in preventing the incoming landslide occurrences and recurrences. In this research, we developed a human-centric framework using information-granules to perform risk assessment of a group of landslides. First, the densitybased spatial clustering of applications with noise (DBSCAN) has been selected as the backbone unsupervised learning method to subclusters for landslide risk indication. The clustering outcomes are visualized via t-distributed stochastic neighbor embedding (t-SNE) in the 2-D embedding space. Second, the prototype points within the subclusters produced by DBSCAN are computed for granular construction. Third, interval-based information-granules are constructed and measured via coverage, specificity and area under the coverage-specificity curve (AUC). Last, with the optimal information-granules constructed, two risk measures namely Value-at-Risk (VaR) and Conditional-Value-at-Risk (CVaR) are computed to interpret the rule-based information-granules with respect to the key attributes. Comparative experiments have also been performed against other benchmarking clustering approaches. Computational results indicate that the information-granules constructed from DBSCAN subclusters offered enhanced performance in reveal meaningful information-granules and provide promising results. The proposed approach can capture the main essence of landslide pattern with higher interpretability and help to reduce the computing overhead.

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“…: IF is in the neighborhood of , THEN should be classified as risk group (15) where is the candidate data point; is the selected prototype;…”

Section: E Information Granules and Risk Measuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Landslide Risk Assessment Using Granular Fuzzy Rule-Based Modeling: A Case Study on Earthquake-Triggered Landslides

Shi

Zeng²,

Tang

et al. 2021

IEEE Access

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“…Therefore, it is of great theoretical and practical importance to study the attribute reduction method of the concept lattice. This method can provide powerful tools for knowledge discovery [7][8][9], data mining [10][11][12], information retrieval [13], semantic networks [14,15], and ontology construction [16,17] based on concept lattice theory.…”

Section: Introductionmentioning

confidence: 99%

Stream Convolution for Attribute Reduction of Concept Lattices

Xu,

Wu,

et al. 2023

Mathematics

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Attribute reduction is a crucial research area within concept lattices. However, the existing works are mostly limited to either increment or decrement algorithms, rather than considering both. Therefore, dealing with large-scale streaming attributes in both cases may be inefficient. Convolution calculation in deep learning involves a dynamic data processing method in the form of sliding windows. Inspired by this, we adopt slide-in and slide-out windows in convolution calculation to update attribute reduction. Specifically, we study the attribute changing mechanism in the sliding window mode of convolution and investigate five attribute variation cases. These cases consider the respective intersection of slide-in and slide-out attributes, i.e., equal to, disjoint with, partially joint with, containing, and contained by. Then, we propose an updated solution of the reduction set for simultaneous sliding in and out of attributes. Meanwhile, we propose the CLARA-DC algorithm, which aims to solve the problem of inefficient attribute reduction for large-scale streaming data. Finally, through the experimental comparison on four UCI datasets, CLARA-DC achieves higher efficiency and scalability in dealing with large-scale datasets. It can adapt to varying types and sizes of datasets, boosting efficiency by an average of 25%.

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“…Three-way decisions [17], also known as trisecting-acting-outcome (TAO) model [18][19][20], is an emerging decision theory for problem solving with uncertainty [21][22][23]. It originates from the semantic explanations for three regions induced by rough sets and has become an emerging theory in the soft computing community [24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Intuitionistic Fuzzy-Based Three-Way Label Enhancement for Multi-Label Classification

2022

Self Cite

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Multi-label classification deals with the determination of instance-label associations for unseen instances. Although many margin-based approaches are delicately developed, the uncertainty classifications for those with smaller separation margins remain unsolved. The intuitionistic fuzzy set is an effective tool to characterize the concept of uncertainty, yet it has not been examined for multi-label cases. This paper proposed a novel model called intuitionistic fuzzy three-way label enhancement (IFTWLE) for multi-label classification. The IFTWLE combines label enhancement with an intuitionistic fuzzy set under the framework of three-way decisions. For unseen instances, we generated the pseudo-label for label uncertainty evaluation from a logical label-based model. An intuitionistic fuzzy set-based instance selection principle seamlessly bridges logical label learning and numerical label learning. The principle is hierarchically developed. At the label level, membership and non-membership functions are pair-wisely defined to measure the local uncertainty and generate candidate uncertain instances. After upgrading to the instance level, we select instances from the candidates for label enhancement, whereas they remained unchanged for the remaining. To the best of our knowledge, this is the first attempt to combine logical label learning with numerical label learning into a unified framework for minimizing classification uncertainty. Extensive experiments demonstrate that, with the selectively reconstructed label importance, IFTWLE achieves statistically superior over the state-of-the-art multi-label classification algorithms in terms of classification accuracy. The computational complexity of this algorithm is On2mk, where n, m, and k denote the unseen instances count, label count, and average label-specific feature size, respectively.

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Double-quantitative distance measurement and classification learning based on the tri-level granular structure of neighborhood system

Cited by 50 publications

References 62 publications

Landslide Risk Assessment Using Granular Fuzzy Rule-Based Modeling: A Case Study on Earthquake-Triggered Landslides

Landslide Risk Assessment Using Granular Fuzzy Rule-Based Modeling: A Case Study on Earthquake-Triggered Landslides

Stream Convolution for Attribute Reduction of Concept Lattices

Intuitionistic Fuzzy-Based Three-Way Label Enhancement for Multi-Label Classification

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