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Adaptive feature selection with weighted fuzzy rough sets for noisy data

Fuzzy Sets and Systems 2025年 518卷

作者： Xiaoling Yang Hongmei Chen Tianrui Li Shan Feng Jihong Wan Yiyu Yao School of Mathematical Sciences Sichuan Normal University China School of Computing and Artificial Intelligence Southwest Jiaotong University China Department of Computer Science University of Regina Regina Canada National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University China

The existence of noise in data may decrease the performance of the classification model. An intuitive assumption suggests that samples with a higher local density have a greater probability of being correctly classified. Moreover, there are differences in the importance of features. It should further increase the influence of important features on the classification task. Motivated by these issues, we propose a sample-driven adaptive feature selection method. It aims to the impact of noise and obtain the adaptive importance of features. In this paper, the Weighted Fuzzy Rough Sets model (WFRS) is proposed by utilizing more reliable samples to address noisy data. Firstly, the approximation margin is derived by applying the WFRS model within the original feature space from the sample's perspective. It serves as a basis for deriving the feature weights. On this basis, the weighted feature space is obtained. This space compresses the features with the lower weight and stretches the features with higher weight. Then, we obtain a specific fuzzy relation to maximize the within-class samples and minimize the between-class samples in the weighted feature space. Further, an evaluation function of features is constructed to model the uncertainty of fuzzy positive and non-negative regions. A Sample-driven adaptive Feature selection algorithm based on the Weighted fuzzy rough sets model (SFW) is designed to capture the varying importance of features by considering the local density of samples. The weighted features are subsequently selected to facilitate the downstream classification tasks. Experimental results show the WFRS model's robustness, the effectiveness of SFW, and its superiority compared to alternative methods.

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Representation learning of reconstructed graphs using random walk graph convolutional network

arXiv

引用

arXiv 2021年

作者： Li, Xing Wei, Wei Feng, Xiangnan Zheng, Zhiming School of Mathematical Science Beihang University Beijing China Key Laboratory of Mathematics Informatics Behavioral Semantics Ministry of Education China Peng Cheng Laboratory ShenzhenGuangdong China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Key Laboratory of Mathematics Informatics Behavioral Semantics Ministry of Education China

Graphs are often used to organize data because of their simple topological structure, and therefore play a key role in machine learning. And it turns out that the low-dimensional embedded representation obtained by graph representation learning are extremely useful in various typical tasks, such as node classification, content recommendation and link prediction. However, the existing methods mostly start from the microstructure (i.e., the edges) in the graph, ignoring the mesoscopic structure (high-order local structure). Here, we propose wGCN – a novel framework that utilizes random walk to obtain the node-specific mesoscopic structures of the graph, and utilizes these mesoscopic structures to reconstruct the graph And organize the characteristic information of the nodes. Our method can effectively generate node embeddings for previously unseen data, which has been proven in a series of experiments conducted on citation networks and social networks (our method has advantages over baseline methods). We believe that combining high-order local structural information can more efficiently explore the potential of the network, which will greatly improve the learning efficiency of graph neural network and promote the establishment of new learning models. © 2021, CC BY.

关键词： Graph neural networks

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Consensus-Based Optimization Methods Converge Globally

arXiv

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arXiv 2021年

作者： Fornasier, Massimo Klock, Timo Riedl, Konstantin Technical University of Munich School of Computation Information and Technology Department of Mathematics Munich Germany Munich Center for Machine Learning Munich Germany Munich Data Science Institute Munich Germany Simula Research Laboratory Department of Numerical Analysis and Scientific Computing Oslo Norway University of San Diego Department of Mathematics San DiegoCA United States

In this paper, we study consensus-based optimization (CBO), which is a multi-agent metaheuristic derivative-free optimization method that can globally minimize nonconvex nonsmooth functions and is amenable to theoretical analysis. Based on an experimentally supported intuition that, on average, CBO performs a gradient descent of the squared Euclidean distance to the global minimizer, we devise a novel technique for proving the convergence to the global minimizer in mean-field law for a rich class of objective functions. The result unveils internal mechanisms of CBO that are responsible for the success of the method. In particular, we prove that CBO performs a convexification of a large class of optimization problems as the number of optimizing agents goes to infinity. Furthermore, we improve prior analyses by requiring mild assumptions about the initialization of the method and by covering objectives that are merely locally Lipschitz continuous. As a core component of this analysis, we establish a quantitative nonasymptotic Laplace principle, which may be of independent interest. From the result of CBO convergence in mean-field law, it becomes apparent that the hardness of any global optimization problem is necessarily encoded in the rate of the mean-field approximation, for which we provide a novel probabilistic quantitative estimate. The combination of these results allows to obtain probabilistic global convergence guarantees of the numerical CBO *** Codes 65K10, 90C26, 90C56, 35Q90, 35Q84 Copyright © 2021, The Authors. All rights reserved.

关键词： Fokker Planck equation

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Transformer-based normative modelling for anomaly detection of early schizophrenia

arXiv

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arXiv 2022年

作者： Da Costa, Pedro F. Dafflon, Jessica Mendes, Sergio Leonardo Sato, João Ricardo Jorge Cardoso, M. Leech, Robert Jones, Emily J.H. Pinaya, Walter H.L. Institute of Psychiatry Psychology & Neuroscience King’s College London United Kingdom Centre for Brain and Cognitive Development Birkbeck College London United Kingdom Data Science and Sharing Team National Institute of Mental Health BethesdaMD United States Machine Learning Team National Institute of Mental Health BethesdaMD United States Center of Mathematics Computing and Cognition Universidade Federal do ABC Brazil School of Biomedical Engineering & Imaging Sciences King’s College London United Kingdom

Despite the impact of psychiatric disorders on clinical health, early-stage diagnosis remains a challenge. Machine learning studies have shown that classifiers tend to be overly narrow in the diagnosis prediction task. The overlap between conditions leads to high heterogeneity among participants that is not adequately captured by classification models. To address this issue, normative approaches have surged as an alternative method. By using a generative model to learn the distribution of healthy brain data patterns, we can identify the presence of pathologies as deviations or outliers from the distribution learned by the model. In particular, deep generative models showed great results as normative models to identify neurological lesions in the brain. However, unlike most neurological lesions, psychiatric disorders present subtle changes widespread in several brain regions, making these alterations challenging to identify. In this work, we evaluate the performance of transformer-based normative models to detect subtle brain changes expressed in adolescents and young adults. We trained our model on 3D MRI scans of neurotypical individuals (N=1,765). Then, we obtained the likelihood of neurotypical controls and psychiatric patients with early-stage schizophrenia from an independent dataset (N=93) from the Human Connectome Project. Using the predicted likelihood of the scans as a proxy for a normative score, we obtained an AUROC of 0.82 when assessing the difference between controls and individuals with early-stage schizophrenia. Our approach surpassed recent normative methods based on brain age and Gaussian Process, showing the promising use of deep generative models to help in individualised analyses. © 2022, CC BY.

关键词： Brain

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Android malware family classification and characterization using CFG and DFG 13

Android malware family classification and characterization u...

引用

13th International Symposium on Theoretical Aspects of Software Engineering, TASE 2019

作者： Xu, Zhiwu Ren, Kerong Song, Fu College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China School of Information Science and Technology ShanghaiTech University Shanghai China

ISBN: (纸本)9781728133423

Android malware has become a serious threat for our daily life, and thus there is a pressing need to effectively mitigate or defend against them. Recently, many approaches and tools to analyze Android malware have been proposed to protect legitimate users from the threat. However, most approaches focus on malware detection, while only a few of them consider malware classification or malware characterization. In this paper, we propose an extension of CDGDroid to classifying and characterizing Android malware families automatically. We first perform static analysis used in CDGDroid to extract control-flow graphs and data-flow graphs on the instruction level. Then we encode the graphs into matrices, and use them to build the family classification models via deep learning. For family characterization, we extract the n-gram sequences from the graphs, which are filtered according to the weights of the classification model built for the target family. And then we construct a vector space model and select the top-k sequences as a characterization of the target family. We have conducted some experiments to evaluate our approach and have identified that the family classification model taking the horizontal combination of CFG and DFG as features offers the best performance in terms of accuracy among all the models. Compared with CDGDroid, Drebin and many antivirus tools gathered in VirusTotal, our family classification model gives a better performance. Finally, We have also conducted experiments on family characterization, and the experimental results have shown that our characterization can capture the malicious behaviors of the testing families. © 2019 IEEE.

关键词： Static analysis

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Correction to: Review of ML and AutoML Solutions to Forecast Time-Series data

引用

Archives of Computational Methods in Engineering 2022年第5期30卷 3473-3473页

作者： Alsharef, Ahmad Aggarwal, Karan Sonia Kumar, Manoj Mishra, Ashutosh Yogananda School of Artificial Intelligence Computing and Data Science Shoolini University Solan India Electronics and Communication Engineering Department Maharishi Markandeshwar (Deemed to be University) Ambala India School of Computer Science University of Petroleum and Energy Studies Dehradun India School of Integrated Technology Yonsei University Seuol South Korea

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BASSET: Bandpass-Adaptive Single-pulse SEarch Toolkit – Optimized Sub-Band Pulse Search Strategies for Faint Narrow-Band FRBs

arXiv

引用

arXiv 2025年

作者： Cao, J.H. Wang, P. Li, D. Pan, Q.H. Mao, K. Niu, C.H. Zhang, Y.K. Qu, Q.Y. Lu, W.J. Zhang, J.S. Zhu, Y.H. Wang, Y.D. Chen, H.X. Chen, X.L. Gügercinoğlu, E. Fang, J.H. Feng, Y. Gao, H. Huang, Y.F. Li, J. Miao, C.C. Tsai, C.W. Yao, J.M. You, S.P. Zhao, R.S. Liu, Q.Z. Weng, S.M. Yew, S.H. Zhang, J. Zhang, L. Zhou, D.K. Zhu, W.W. National Astronomical Observatories Chinese Academy of Sciences 20A Datun Road Chaoyang District Beijing100101 China University of Chinese Academy of Sciences Beijing100049 China Institute for Frontiers in Astronomy and Astrophysics Beijing Normal University Beijing102206 China Department of Astronomy Tsinghua University Beijing100084 China Zhejiang Lab Zhejiang Hangzhou311121 China Central China Normal University Wuhan430079 China Institute for Astronomy School of Physics Zhejiang University Hangzhou310027 China Department of Astronomy Beijing Normal University Beijing100875 China School of Astronomy and Space Science Nanjing University Nanjing210023 China Ministry of Education Nanjing210023 China Department of Astronomy School of Physical Sciences University of Science and Technology of China Hefei230026 China Xinjiang Astronomical Observatory Chinese Academy of Sciences Urumqi830011 China Key Laboratory of Information and Computing Science Guizhou Province Guizhou Normal University Guiyang550001 China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal University Guiyang550001 China Purple Mountain Observatory Chinese Academy of Sciences Nanjing210023 China College of Physics and Electronic Engineering Qilu Normal University Jinan250200 China School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Centre for Astrophysics and Supercomputing Swinburne University of Technology P.O. Box 218 HawthornVIC3122 Australia

The existing single-pulse search algorithms for fast radio bursts (FRBs) do not adequately consider the frequency bandpass pattern of the pulse, rendering them incomplete for the relatively narrow-spectrum detection of pulses. We present a new search algorithm for narrow-band pulses to update the existing standard pipeline, Bandpass-Adaptive Single-pulse SEarch Toolkit (BASSET). The BASSET employs a time-frequency correlation analysis to identify and remove the noise involved by the zero-detection frequency band, thereby enhancing the signal-to-noise ratio (SNR) of the pulses. The BASSET algorithm was implemented on the FAST real dataset of FRB 20190520B, resulting in the discovery of additional 79 pulses through reprocessing. The new detection doubles the number of pulses compared to the previously known 75 pulses, bringing the total number of pulses to 154. In conjunction with the pulse calibration and the Markov Chain Monte Carlo (MCMC) simulated injection experiments, this work updates the quantified parameter space of the detection rate. Moreover, a parallel-accelerated version of the BASSET code was provided and evaluated through simulation. BASSET has the capacity of enhancing the detection sensitivity and the SNR of the narrow-band pulses from the existing pipeline, offering high performance and flexible applicability. BASSET not only enhances the completeness of the low-energy narrow-band pulse detection in a more robust mode, but also has the potential to further elucidate the FRB luminosity function at a wider energy scale. Copyright © 2025, The Authors. All rights reserved.

关键词： Signal to noise ratio

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Hierarchical Decomposition and Prescribed Performance Bound based Adaptive Control for Leader-Follower Formation of Uncertain Nonholonomic Mobile Robots

Hierarchical Decomposition and Prescribed Performance Bound ...

引用

International Conference on Control, Automation, Robotics and Vision (ICARCV)

作者： Jiajun Shen Wei Wang Jinhu Lü Chao Deng School of Automation Science and Electrical Engineering Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China School of Electrical and Electronic Engineering Nanyang Technological University Singapore

ISBN: (数字)9781728177090

ISBN: (纸本)9781728177106

In this paper, the cooperative formation control problem is investigated for multiple two-wheeled mobile robots with unknown parameters. By combining the hierarchical decomposition and prescribed performance bound (PPB) technique, distributed adaptive formation controllers are designed based on dynamic surface control. It is proved in the Lyapunov sense that all the closed-loop signals are semi-globally bounded and the tracking error converges to a compact set, which can be made arbitrarily small by adjusting the design parameters appropriately. Furthermore, formation maintenance, connectivity preservation and collision avoidance can be achieved with the proposed control scheme. Simulations are also given to verify the effectiveness of the theoretical results.

关键词： Mobile robots Robot kinematics Wheels Collision avoidance Directed graphs Kinematics Information processing

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The Method for Solving Bi-matrix Games with Intuitionistic Fuzzy Set Payoffs 1

引用

3rd East Asia Game Theory International Conference, EAGT 2019

作者： Nan, Jiang-Xia Zhang, Li Li, Deng-Feng School of Mathematics and Computing Science Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology GuilinGuangxi541004 China School of Economics and Management Fuzhou University FuzhouFujian350108 China

ISBN: (数字)9789811506574

ISBN: (纸本)9789811506567

The aim of this paper is to develop a bilinear programming method for solving bi-matrix games in which the payoffs are expressed with intuitionistic fuzzy sets (IFSs), which are called IFS bi-matrix games for short. In this method, using the equivalent relation between IFSs and interval-valued fuzzy sets (IVFSs) and the operations of IVFSs, we propose a new order relation of IFSs through introducing a ranking function, which is proven to be a total order relation. Hereby we introduce the concepts of solutions of IFS bi-matrix games and parametric bi-matrix games. It is proven that any IFS bi-matrix game has at least one satisfying Nash equilibrium solution, which is equivalent to the Nash equilibrium solution of corresponding parametric bi-matrix game. The latter can be obtained through solving the auxiliary parametric bilinear programming model. The models and method proposed in this paper are demonstrated with a real example of the e-commerce retailers’ strategy choice problem. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Game theory

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1298 THE IMPACT OF ACUTE HEALTHCARE UTILISATION ON FUNCTIONAL DECLINE IN OLDER ADULTS OVER TIME: A POPULATION-BASED COHORT STUDY

引用

Age and Ageing 2023年第SUPPLEMENT_1期52卷

作者： Conneely, M Leahy, S Trépel, D Robinson, K Boland, F Moriarty, F Galvin, R School of Allied Health Ageing Research Centre Health Research Institute University of Limerick Ireland Department of Sport Exercise and Nutrition School of Science and Computing Atlantic Technological University ATU Galway City Ireland Trinity Institute of Neurosciences Trinity College Dublin Ireland Data Science Centre and Department of General Practice Royal College of SurgeonsIreland Dublin 2 School of Pharmacy and Biomolecular Sciences Royal College of SurgeonsIreland Dublin 2

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