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Small dataset augmentation with radial basis function approximation for causal discovery using constraint-based method

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ETRI JOURNAL 2025年第1期47卷 90-101页

作者： Jung, Chan Young Jang, Yun Sejong Univ Comp Sci & Engn Convergence Engn Intelligent Drone Seoul South Korea

Causal analysis involves analysis and discovery. We consider causal discovery, which implies learning and discovering causal structures from available data, owing to the significance of interpreting causal relationships in various fields. Research on causal discovery has been primarily focused on constraint- and score-based interpretable methods rather than on methods based on complex deep learning models. However, identifying causal relationships in real-world datasets remains challenging. Numerous studies have been conducted using small datasets with established ground truths. Moreover, constraint-based methods are based on conditional independence tests. However, such tests have a lower statistical power when applied to small datasets. To solve the small sample size problem, we propose a model that generates a continuous function from available samples using radial basis function approximation. We address the problem by extracting data from the generated continuous function and evaluate the proposed method on both real and synthetic datasets generated by structural equation modeling. The proposed method outperforms constraint-based methods using only small datasets.

关键词： causal discovery conditional independence constraint-based method data augmentation radial basis function approximation

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An iterative conditional variable selection method for constraint-based time series causal discovery

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2025年 260卷

作者： Wang, Zihang Li, Shuai Zhou, Xiaofeng Zhu, Shijie Chinese Acad Sci Key Lab Networked Control Syst Shenyang 110016 Peoples R China Chinese Acad Sci Shenyang Inst Automat Shenyang 110016 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Shenyang Univ Chem Technol Coll Informat Engn Shenyang 110142 Peoples R China

Time series causal discovery aims to identify cause-effect relationships among variables from time series data, providing valuable insights into complex real-world scenarios. However, existing constraint-based causal discovery methods face challenges such as limited detection power, stemming from issues like dimensionality explosion and uncertainty caused by indirect paths. To address these problems, we propose a novel iterative conditional variable selection method designed for lagged, linear, and nonlinear causal discovery in time series. (1) Firstly, we block indirect information while minimizing the dimensionality of the conditioning set. Specifically, our method selects the parent set of each target variable as the conditioning set, which includes only those variables involved in the indirect path. (2) Then, we refine the conditioning set by selecting a subset of the parent set for each target variable to focus on indirect causal relationships. (3) Finally, the iterative application of steps (1) and (2) progressively corrects the indirect paths, leading to a significant improvement in detection power. Experimental results on synthetic and public datasets, as well as for varying time lags, node counts, and a chemical fault diagnosis case, demonstrate that our method outperforms state-of-the-art (SOTA) approaches.

关键词： Iterative conditional variable selection Causal discovery Time series Detection power constraint-based method

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A Nonsmooth Newton Solver for Capturing Exact Coulomb Friction in Fiber Assemblies

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ACM TRANSACTIONS ON GRAPHICS 2011年第1期30卷 1–14页

作者： Bertails-Descoubes, Florence Cadoux, Florent Daviet, Gilles Acary, Vincent INRIA Sophia Antipolis France

We focus on the challenging problem of simulating thin elastic rods in contact, in the presence of friction. Most previous approaches in computer graphics rely on a linear complementarity formulation for handling contact in a stable way, and approximate Coulombs's friction law for making the problem tractable. In contrast, following the seminal work by Alart and Curnier in contact mechanics, we simultaneously model contact and exact Coulomb friction as a zero finding problem of a nonsmooth function. A semi-implicit time-stepping scheme is then employed to discretize the dynamics of rods constrained by frictional contact: this leads to a set of linear equations subject to an equality constraint involving a nondifferentiable function. To solve this one-step problem we introduce a simple and practical nonsmooth Newton algorithm which proves to be reasonably efficient and robust for systems that are not overconstrained. We show that our method is able to finely capture the subtle effects that occur when thin elastic rods with various geometries enter into contact, such as stick-slip instabilities in free configurations, entangling curls, resting contacts in braid-like structures, or the formation of tight knots under large constraints. Our method can be viewed as a first step towards the accurate modeling of dynamic fibrous materials.

关键词： Algorithms Performance Modeling simulation contact Coulomb friction dynamics of thin elastic rods constraint-based method knot tying hair simulation

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Effective and scalable causal partitioning based on low-order conditional independent tests

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NEUROCOMPUTING 2020年 389卷 146-154页

作者： Yan, Chuanxu Zhou, Shuigeng Fudan Univ Sch Comp Sci Shanghai 200433 Peoples R China Fudan Univ Shanghai Key Lab Intelligent Informat Proc Shanghai 200433 Peoples R China

Recovering causal relationships from observed data is crucial to a variety of applications. Due to the curse of dimensionality, general causal discovery methods such as constraint-based methods and functional model based methods are not quite effective and efficient for large and high-dimensional data sets. Thus, some causal partitioning methods have been proposed to handle this problem. However, existing causal partitioning methods rely on high-order conditional independent (CI) tests, which makes them inefficient in handling dense causal graphs. Therefore, high-dimensionality is still a big challenge to these methods. In this work, we propose a new split-and-merge strategy to enable effective and scalable causality discovery. Different from the existing methods, our method uses only low-order CI tests, can get more accurate results and is applicable to various scenarios. We provide both theoretic analysis and empirical evaluation on the proposed method. Experiments on various real-world causal graphs show that the proposed method outperforms the stat-of-the-art method in terms of accuracy, efficiency and scalability. For high-dimensional cases, our method is much faster than the counterpart by one to three orders of magnitudes. (C) 2020 The Authors. Published by Elsevier B.V.

关键词： Causal inference High-dimensionality constraint-based method Conditional independence test

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CECM: Adding Pairwise constraints To Evidential Clustering

CECM: Adding Pairwise Constraints To Evidential Clustering

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2010 IEEE World Congress on Computational Intelligence

作者： Antoine, Violaine Quost, Benjamin Masson, Marie-Helene Denoeux, Thierry Heudiasyc Laboratory University of Technology of Compi&#x00E8 gne Centre de Recherche de Royallieu BP20529 60205 Compi&#x00E8 gne France

ISBN: (纸本)9781424469208

Fuzzy or hard partitioning methods aim at grouping objects according to their similarity. Recently, a new concept of partition based on belief function theory, called credal partition, has been proposed and has been shown to generate meaningful description of the data. Hard, fuzzy or credal partitions are generally obtained using unsupervised learning methods, using only the numeric description between two objects to compute their similarity. However, in some applications, some kind of background knowledge about the objects or about the clusters is available. To integrate this auxiliary information, constraint-based (or semi-supervised) methods have been proposed. A popular type of constraints specifies whether two objects are in the same cluster (must-link) or in different clusters (cannot-link). We propose here a new algorithm, called CECM, which computes a credal partition using a constrained clustering method. We show how to translate the available information into constraints, and how to integrate them in the search of the credal partition. The paper ends with some experimental results. Results of CECM are compared to other constrained clustering algorithms. Then an application in image segmentation is described.

关键词： CECM belief function theory belief networks constrained clustering method constraint-based method credal partition data mining evidential clustering fuzzy partitioning method fuzzy set theory hard partitioning method pairwise constraint pattern clustering semi-supervised method unsupervised learning unsupervised learning

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Detecting and Fixing UML Model Inconsistencies using constraints 4

Detecting and Fixing UML Model Inconsistencies using Constra...

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4th IEEE International Colloquium on Information Science and Technology (CiSt)

作者： Allaki, Driss Dahchour, Mohamed En-nouaary, Abdeslam Inst Natl Postes & Telecommun Rabat Morocco

ISBN: (纸本)9781509007516

Complex software systems are often modeled as a collection of related UML diagrams, each of which describes particular aspects of the system being investigated. These diagrams might contain inconsistencies due to the evolving nature of software systems and to the refinement of the models across the software life cycle. It is so mandatory to discover the potential inconsistencies in UML models as soon as possible before implementing the system. This paper proposes a method for checking the consistency of UML models, based on formal constraints defined at the meta-model of UML. These constraints are described using EVL (Epsilon Validation Language) by matching related diagrams features at the meta-level. EVL also helps repair and correct the inconsistencies being detected. Our method is easily automated and is complete in terms of coverage of both potential inconsistencies and the UML diagrams commonly used.

关键词： Inconsistency management Checking inconsistencies constraint-based method UML models

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Accelerating Recursive Partition-based Causal Structure Learning 21

Accelerating Recursive Partition-Based Causal Structure Lear...

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International Conference on Autonomous Agents and Multiagent Systems

作者： Md Musfiqur Rahman Ayman Rasheed Md. Mosaddek Khan Mohammad Ali Javidian Pooyan Jamshidi Md. Mamun-Or-Rashid Department of Computer Science and Engineering University of Dhaka School of Electrical and Computer Engineering Purdue University Department of Computer Science and Engineering University of South Carolina

ISBN: (纸本)9781713832621

Causal structure discovery from observational data is fundamental to the causal understanding of autonomous systems such as medical decision support systems, advertising campaigns and self-driving cars. This is essential to solve well-known causal decision making and prediction problems associated with those real-world applications. Recently, recursive causal discovery algorithms have gained particular attention among the research community due to their ability to provide good results by using Conditional Independent (CI) tests in smaller sub-problems. However, each of such algorithms needs a refinement function to remove undesired causal relations of the discovered graphs. Notably, with the increase of the problem size, the computation cost (i.e., the number of CI-tests) of the refinement function makes an algorithm expensive to deploy in practice. This paper proposes a generic causal structure refinement strategy that can locate the undesired relations with a small number of CI-tests, thus speeding up the algorithm for large and complex problems. We theoretically prove the correctness of our algorithm. We then empirically evaluate its performance against the state-of-the-art algorithms in terms of solution quality and completion time in synthetic and real datasets.

关键词： Causal discovery High-dimensionality constraint-based method Conditional independence tests algorithms Recursive Refining decision support systems Autonomous Systems Completion time acceleration accuracy class observational data

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