Article
Computer Science, Information Systems
Yangxue Li, Enrique Herrera-Viedma, Gang Kou, Juan Antonio Morente-Molinera
Summary: This paper proposes a Z-number-valued rule-based decision tree (ZRDT) and provides the learning algorithm. Compared with other classical decision trees, ZRDT performs better in terms of classification accuracy and decision tree size. ZRDT uses information gain to select features in each rule instead of fuzzy confidence, and generates a second fuzzy number with negative samples to improve the model's fit to the training data. Based on statistical tests, ZRDT achieves the highest classification performance with the smallest size for the produced decision tree.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Tinghui Ouyang
Summary: In this paper, a new rule-based modeling approach is proposed to analyze the dynamic behaviors of complex systems in the era of big data. This approach incorporates structural information mining and granular computing, and uses DBSCAN and granular fuzzy intervals to reflect system behaviors on uncertainty. Experimental analysis demonstrates the superiority of the proposed approach in various design scenarios.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Long-Hao Yang, Jun Liu, Fei-Fei Ye, Ying-Ming Wang, Chris Nugent, Hui Wang, Luis Martinez
Summary: This study aims to design a novel rule-based system called Cumulative Belief Rule-Based System (CBRBS). By establishing efficient rule-base modeling and inference procedures, CBRBS achieves a balance of explainability, high-efficiency, and accuracy, overcoming the limitations of classical rule-based systems. Extensive experiments illustrate the features and advantages of CBRBS over other systems.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Psychology, Mathematical
Zhiya Liu, Yitao Zhang, Ding Ma, Qunfang Xu, Carol A. Seger
Summary: The study compared rule-based and information-integration category learning with point-valued feedback, finding that point-valued feedback led to better learning overall. Participants in the information-integration task reached criterion fastest when they received both gains and losses, while in the rule-based task, participants reached criterion most rapidly when they received either gains or losses, but not both together.
PSYCHONOMIC BULLETIN & REVIEW
(2021)
Article
Computer Science, Interdisciplinary Applications
Weixi Wang, Han Guo, Xiaoming Li, Shengjun Tang, You Li, Linfu Xie, Zhihan Lv
Summary: This study aims to improve construction efficiency and ensure the infrastructure needs of urban development. By applying digital twins and three-dimensional modeling, intelligent manufacturing of buildings is realized. The proposed model shows advantages in performance and scalability, and provides important reference for the intelligent development of the construction industry and high-quality building development.
JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION
(2022)
Article
Physics, Multidisciplinary
Mohammad Azad, Igor Chikalov, Shahid Hussain, Mikhail Moshkov, Beata Zielosko
Summary: This study examines decision trees with hypotheses and compares their extracted decision rules with those from conventional decision trees, finding that decision rules derived from decision trees with hypotheses are often superior.
Article
Computer Science, Artificial Intelligence
Xiaohe Zhang, Degang Chen, Jusheng Mi
Summary: This article introduces a fuzzy decision rule-based online classification algorithm called OFRCA, which combines online learning theory and Formal Concept Analysis (FCA) in a fuzzy formal decision context. The algorithm obtains weight vectors for attributes and incremental fuzzy decision rules through a fusion process. The weight vector is updated based on a loss function, and the final attribute-weighted classifier is formed by fusing all the rules. Numerical experiments show that OFRCA achieves the highest advanced classification performance.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Fang Zhao, Hongyue Guo, Lidong Wang
Summary: A novel modeling method is established in this study to generate fuzzy rules based on experimental evidence, utilizing the boundary erosion algorithm for input sample clustering and the principle of justifiable granularity for output granulation. Models with finer information granules are designed for rule extraction in each cluster to examine performance under different granularity levels. The rule-based models incorporating information granules perform better in analyzing dataset structures and have potential applications in ship management.
Article
Computer Science, Software Engineering
Dirk Streeb, Yannick Metz, Udo Schlegel, Bruno Schneider, Mennatallah El-Assady, Hansjoerg Neth, Min Chen, Daniel A. Keim
Summary: This article provides an overview of visualizations for decision tree classifiers, focusing on the differences in visualizations for 16 tasks. The study finds that existing interactive visual analytics systems offer a variety of visual designs for classifier development, but there is limited coverage of utilization tasks and visual representations of quality measures other than accuracy. The authors suggest integrating algorithmic techniques, mathematical quality measures, and tailored interactive visualizations to enhance the utilization of knowledge by human experts.
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
(2022)
Article
Construction & Building Technology
Jiaming Wu, Jian Chen, Guoliang Chen, Zhe Wu, Yu Zhong, Bin Chen, Wenhui Ke, Juehao Huang
Summary: With the rapid development of infrastructure construction, geotechnical engineering has gained attention due to its complexity and diversity. Accelerating the informatization of geotechnical engineering can enhance project management, but the lack of unified data standards poses challenges for information integration.
ADVANCES IN CIVIL ENGINEERING
(2021)
Article
Multidisciplinary Sciences
Chunxue Wang, Daming Zhang, Jian Yue, Xucheng Zhang, Hang Lin, Xiangyi Sun, Anqi Cui, Tong Zhang, Changming Chen, Teng Fei
Summary: Information encryption technique is widely used in individual privacy, military confidentiality, and national security. However, traditional electronic encryption approaches are unable to meet the demands of high-speed data transmission. Optical encryption technology based on optical pulse-code modulation is proposed in this study to ensure secure transmission of vital information.
NATURE COMMUNICATIONS
(2023)
Article
Automation & Control Systems
Ren Li, Tianjin Mo, Jianxi Yang, Shixin Jiang, Tong Li, Yiming Liu
Summary: This article introduces a novel model called the bridge structure and health monitoring ontology, utilizing Semantic Web technologies to achieve fine-grained modeling of bridge structures, SHM systems, sensors, and sensory data. It addresses the serious data island problems in traditional SHM solutions and demonstrates the usefulness of a bridge SHM big data platform.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Clinical Neurology
Arndis Simonsen, Riccardo Fusaroli, Malte Lau Petersen, Arnault-Quentin Vermillet, Vibeke Bliksted, Ole Mors, Andreas Roepstorff, Daniel Campbell-Meiklejohn
Summary: An abnormality in inference, where patients with schizophrenia overweigh and overcount direct experiences while underweighting information from others, may lead to hallucinations, delusions, and negative symptoms, particularly impacting patients with social cognitive deficits.
Article
Engineering, Industrial
Sahin Akin, Oguzcan Ergun, Elif Surer, Ipek Gursel Dino
Summary: This research explores the use of immersive environments in performative architectural design processes to enhance daylighting performance. The study introduces a novel immersive tool, HoloArch, which integrates building information modeling, performative simulations, and interactive design processes. The findings show that HoloArch effectively improves daylighting performance and provides augmented perception for designers.
ENGINEERING CONSTRUCTION AND ARCHITECTURAL MANAGEMENT
(2021)
Article
Computer Science, Information Systems
Zhouming Ma, Jusheng Mi, Yiting Lin, Jinjin Li
Summary: Variable precision rough set (VPRS) has been widely studied as an essential way of knowledge representation and acquisition in uncertainty theory. This paper investigates the corresponding CVPRS model based on a covering-based rough set model, and systematically studies its algebraic structures and properties. An attribute reduction approach is proposed for a covering-based decision information system using the CVPRS model, and the performances of different boundary operators and related indices in these reduction methods are compared. Necessity rules and possibility rules extraction methods corresponding to decision classes are established, and their validity and security are theoretically verified.
INFORMATION SCIENCES
(2022)
Article
Behavioral Sciences
Edna C. Cieslik, Markus Ullsperger, Martin Gell, Simon B. Eickhoff, Robert Langner
Summary: Previous studies on error processing have primarily focused on the posterior medial frontal cortex, but the role of other brain regions has been underestimated. This study used activation likelihood estimation meta-analyses to explore brain activity related to committing errors and responding successfully in interference tasks. It was found that the salience network and the temporoparietal junction were commonly involved in both correct and incorrect responses, indicating their general involvement in coping with situations that require increased cognitive control. Error-specific convergence was observed in the dorsal posterior cingulate cortex, posterior thalamus, and left superior frontal gyrus, while successful responding showed stronger convergence in the dorsal attention network and lateral prefrontal regions. Underrecruitment of these regions in error trials may reflect failures in activating the appropriate stimulus-response contingencies necessary for successful response execution.
NEUROSCIENCE AND BIOBEHAVIORAL REVIEWS
(2024)
