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A data representation method using distance correlation

Frontiers of Computer Science 2025年第1期19卷 1-14页

作者： Xinyan LIANG Yuhua QIAN Qian GUO keyin ZHENG Institute of Big Data Science and Industry Shanxi UniversityTaiyuan 030006China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi UniversityTaiyuan 030006China School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan 030024China Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and TechnologyTaiyuan 030024China

Association in-between features has been demonstrated to improve the representation ability of data. However, the original association data reconstruction method may face two issues: the dimension of reconstructed data is undoubtedly higher than that of original data, and adopted association measure method does not well balance effectiveness and efficiency. To address above two issues, this paper proposes a novel association-based representation improvement method, named as AssoRep. AssoRep first obtains the association between features via distance correlation method that has some advantages than Pearson’s correlation coefficient. Then an improved matrix is formed via stacking the association value of any two features. Next, an improved feature representation is obtained by aggregating the original feature with the enhancement matrix. Finally, the improved feature representation is mapped to a low-dimensional space via principal component analysis. The effectiveness of AssoRep is validated on 120 datasets and the fruits further prefect our previous work on the association data reconstruction.

关键词： association representation distance correlation classification

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Large-Scale Multi-Objective Optimization Algorithm Based on Weighted Overlapping Grouping of Decision Variables

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Computer Modeling in Engineering & Sciences 2024年第7期140卷 363-383页

作者： Liang Chen Jingbo Zhang Linjie Wu Xingjuan Cai Yubin Xu Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and TechnologyTaiyuan030024China School of State Key Laboratory of Novel Software Technology Nanjing UniversityNanjing210008China

The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision ***,in practical problems,the interaction among decision variables is intricate,leading to large group sizes and suboptimal optimization effects;hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables(MOEAWOD)is proposed in this ***,the decision variables are perturbed and categorized into convergence and diversity variables;subsequently,the convergence variables are subdivided into groups based on the interactions among different decision *** the size of a group surpasses the set threshold,that group undergoes a process of weighting and overlapping ***,the interaction strength is evaluated based on the interaction frequency and number of objectives among various decision *** decision variable with the highest interaction in the group is identified and disregarded,and the remaining variables are then reclassified into ***,the decision variable with the strongest interaction is added to each *** minimizes the interactivity between different groups and maximizes the interactivity of decision variables within groups,which contributed to the optimized direction of convergence and diversity exploration with different *** was subjected to testing on 18 benchmark large-scale optimization problems,and the experimental results demonstrate the effectiveness of our *** with the other algorithms,our method is still at an advantage.

关键词： Decision variable grouping large-scale multi-objective optimization algorithms weighted overlapping grouping direction-guided evolution

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A Multi-Objective Artificial Physics Optimization Algorithm Based on Two-Phase Search 8

A Multi-Objective Artificial Physics Optimization Algorithm ...

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8th International Symposium on Computer Science and Intelligent Control, ISCSIC 2024

作者： Zhang, Huihua Xie, Liping Taiyuan University of Science and Technology Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan China

ISBN: (纸本)9798350380286

Aiming at the problems of insufficient utilization of information about elite particles in archive and instability of particle motion in the population in the multi-objective artificial physics optimization algorithm (MOAPO) in solving multiobjective optimization problems, A multi-objective artificial physics optimization algorithm based on two-phase search (TPMOAPO) is proposed. To begin with, the algorithm improves the calculation of the mass of particles, so that the strength and weakness of the particles can be accurately transformed into the corresponding masses while improving the efficiency of particle mass calculation. Next, a two-phase search strategy is proposed, which makes the algorithm have strong exploration ability in the first phase, and the second phase gradually enhances the exploitation capability with iterations, which solves the problem of instability motion of particles in the search process. Finally, the simulated binary crossover (SBX) and polynomial-based mutation (PM) operators are adopted in the archive to further enhance the search capability of the algorithm. For verifying the performance of TP-MOAPO, 21 benchmark functions were selected to compare with the classical multi-objective particle swarm optimization algorithms: MOPSO, dMOPSO, SMPSO, MMOPSO, and NMPSO, and the experimental results show the superiority of TP-MOAPO in these functions. © 2024 IEEE.

关键词： Optimization algorithms

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Ensemble Strategy Based Hyper-heuristic Evolutionary Algorithm for Many-Objective Optimization 13th

Ensemble Strategy Based Hyper-heuristic Evolutionary Algorit...

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13th IFIP TC 12 International Conference on Intelligent Information Processing, IIP 2024

作者： Qian, Wang Jingbo, Zhang Zhihua, Cui Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and Technology Shanxi Taiyuan030024 China

ISBN: (纸本)9783031578076

Many-objective Optimization problems (MaOPs), with four or more objectives are difficult to solve, is a kind of common optimization problems in actual industrial production. In recent years, a large number of many-objective optimization algorithms (MaOEAs) have been proposed to solve various types of MaOPs. However, in practical problems, it is usually hard to improve the existing optimization algorithms or make a lot of attempts for MaOEAs because the true Pareto surface is usually unknown in a new MaOPs, which is a time-consuming and uncertain task. In this paper, inspired by the selective hyper heuristic optimization algorithm, we propose an integrated hyper-heuristic many-objective optimization algorithm (MaOEA-EH), which can integrate the existing advanced MaOEAs by simulating the PBFT consensus mechanism in the blockchain, and select the best algorithm for the current problem through the voting-election method in the iterative process. Numerical results show that our algorithm performs well on various many-objective problems. © IFIP International Federation for Information Processing 2024.

关键词： Evolutionary algorithms

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A Progressive Skip Reasoning Fusion Method for Multi-Modal Classification 24

A Progressive Skip Reasoning Fusion Method for Multi-Modal C...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Guo, Qian Liang, Xinyan Qian, Yuhua Cui, Zhihua Wen, Jie Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and Technology Taiyuan China Institute of Big Data Science and Industry Shanxi University Taiyuan China

ISBN: (纸本)9798400706868

In multi-modal classification tasks, a good fusion algorithm can effectively integrate and process multi-modal data, thereby significantly improving its performance. Researchers often focus on the design of complex fusion operators and have proposed numerous fusion operators, while paying less attention to the design of feature fusion usage, specifically how features should be fused to better facilitate multi-modal classification tasks. In this article, we propose a progressive skip reasoning fusion network (PSRFN) to make some attempts to address this issue. Firstly, unlike most existing multi-modal fusion methods that only use one fusion operator in a single stage to fuse all view features, PSRFN utilizes the progressive skip reasoning (PSR) block to fuse all views with a fusion operator at each layer. Specifically, each PSR block utilizes all view features and the fused features from the previous layer to jointly obtain the fused features for the current layer. Secondly, each PSR block utilizes a dual-weighted fusion strategy with learnable parameters to adaptively allocate weights during the fusion process. The first level of weighting assigns weights to each view feature, while the second level assigns weights to the fused features from the previous layer and the fused features obtained from the first level of weighting in the current layer. This strategy ensures that the PSR block can dynamically adjust the weights based on the actual contribution of features. Finally, to enable the model to fully utilize feature information from different levels for feature fusion, the skip connections are adopted between PSR blocks. Extensive experiment results on six real multi-modal datasets show that a better usage for fusion operator is indeed able to improve performance. © 2024 ACM.

关键词： Classification (of information)

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Tensor joint framework enhanced by game-theoretic constrained optimization for skin cancer forecasting

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Swarm and Evolutionary Computation 2025年 96卷

作者： Jinqian Yang Jie Wen Haochen Zhao Jianghui Cai Xingjuan Cai Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and Technology Taiyuan 030024 China School of Computer Science and Technology Taiyuan University of Science and Technology Taiyuan 030024 China School of Computer Science and Technology North University of China Taiyuan 030051 China

Skin cancer is a public health concern due to its high incidence and detection challenges. While tensor decomposition is widely utilized to predict miRNA-disease associations, existing models are not optimized for skin cancer, thereby limiting their comprehensiveness and interpretability. To address these limitations, we propose a many-objective tensor joint framework aimed at enhancing prediction accuracy while maintaining comprehensiveness and interpretability. The proposed framework classifies tensors according to their similarity to skin cancer and employs a many-objective optimization algorithm to optimize weight allocation. Similarity constraints for skin cancer are applied to prioritize relevant information, effectively minimizing noise from unrelated diseases. Furthermore, we introduce a multi-stage constrained many-objective optimization algorithm based on game theory. This algorithm leverages game theory to dynamically adjust population diversity, convergence, and constraint violations throughout the evolutionary process, thereby improving the overall framework’s performance. Experimental results demonstrate that the proposed algorithm outperforms existing state-of-the-art constrained many-objective optimization algorithms on benchmarks such as MW and DTLZ. Compared to other tensor decomposition methods, the proposed framework achieves a comprehensive improvement ranging from 8.49% to 11.53% across four objectives.

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An in-depth Exploration of LAMOST Unknown Spectra Based on Density Clustering

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Research in Astronomy and Astrophysics 2023年第5期23卷 52-65页

作者： Hai-Feng Yang Xiao-Na Yin Jiang-Hui Cai Yu-Qing Yang A-Li Luo Zhong-Rui Bai Li-Chan Zhou Xu-Jun Zhao Ya-Ling Xun Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and TechnologyTaiyuan 030024China School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan 030024China School of Computer Science and Technology North University of ChinaTaiyuan 030051China National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China

Large sky Area Multi-Object fiber Spectroscopic Telescope(LAMOST) has completed the observation of nearly 20 million celestial objects,including a class of spectra labeled “Unknown.” Besides low signal-to-noise ratio,these spectra often show some anomalous features that do not work well with current *** this paper,a total of 637,889 “Unknown” spectra from LAMOST DR5 are selected,and an unsupervised-based analytical framework of “Unknown” spectra named SA-Frame(Spectra analysis-Frame) is provided to explore their origins from different *** SA-Frame is composed of three parts:NAPC-Spec clustering,characterization and origin ***,NAPC-Spec(Nonparametric density clustering algorithm for spectra) characterizes different features in the “unknown” spectrum by adjusting the influence space and divergence distance to minimize the effects of noise and high dimensionality,resulting in 13 ***,characteristic extraction and representation of clustering results are carried out based on spectral lines and continuum,where these 13 types are characterized as regular spectra with low S/Ns,splicing problems,suspected galactic emission signals,contamination from city light and un-gregarious type ***,a preliminary analysis of their origins is made from the characteristics of the observational targets,contamination from the sky,and the working status of the *** results would be valuable for improving the overall data quality of large-scale spectral surveys.

关键词： methods data analysis-surveys-techniques spectroscopic-site testing-methods analytical

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A Multi-Objective Artificial Physics Optimization Algorithm Based on Two-Phase Search

A Multi-Objective Artificial Physics Optimization Algorithm ...

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International Symposium on Computer Science and Intelligent Controls (ISCSIC)

作者： Huihua Zhang Liping Xie Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and Technology Taiyuan China

ISBN: (数字)9798350380286

ISBN: (纸本)9798350380293

关键词： Measurement Computer science Benchmark testing Search problems Particle swarm optimization Optimization Physics Intelligent control Convergence

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A Structural Anomaly Detection Method Based on Fusion Interval Representation of Multi-Grained Morphological Information

SSRN

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SSRN 2024年

作者： Yang, Yuqing Cai, Jianghui Yang, Haifeng Shi, Chenhui Zhang, Min Zhao, Xujun Xun, Yaling Zhou, Lichan Shanxi Taiyuan030024 China Shanxi Taiyuan030024 China Shanxi Taiyuan030051 China Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Shanxi Taiyuan030024 China

Complex morphological representation is essential for structural anomaly detection of sequence data. Intervals based representations can portray global structure in structural anomaly detection by analysing internal correlations among various patterns. However, current interval-based representations ignore local complex morphological information, failing to capture detail morphological structure. And the influence of longitudinal data dispersion on the construction of intervals is neglected, generating incomplete global morphological information. Therefore, this paper proposes a fusion interval representation of multi-grained morphological information for detecting structural anomalies in sequence data. First, we propose a method for extracting local morphological information from sequence data points. We use local binary pattern (LBP) to fuse the morphological information of neighborhood data and obtain high-order local differential data (HLDD) with richer local morphological information. Besides, an adaptively numerical dispersion interval representation method is designed to portray the global morphological information. We define sparse degree (SD) to reflect sparsity of longitudinally dispersion. It is used to adjust the interval boundaries to satisfy principle of justifiable granularity, and compact intervals are generated to portray global morphological information. Finally, we obtain final intervals by fusing multi-grained morphological information to represent anomaly patterns. The performance of our method on anomaly detection improves by about 10% compared with the existing methods, which illustrates the advantages of our method. The code is available at https://***/LichanZhou/AIR. © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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Intersection-union dual-stream cross-attention Lova-SwinUnet for skin cancer hair segmentation and image repair

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Computers in Biology and Medicine 2024年 180卷 108931页

作者： Qin, Juanjuan Pei, Dong Guo, Qian Cai, Xingjuan Xie, Liping Zhang, Wensheng Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and Technology Taiyuan China State Key Laboratory for Novel Software Technology at Nanjing University Nanjing University Nanjing China Beijing China

Skin cancer images have hair occlusion problems, which greatly affects the accuracy of diagnosis and classification. Current dermoscopic hair removal methods use segmentation networks to locate hairs, and then uses repair networks to perform image repair. However, it is difficult to segment hair and capture the overall structure between hairs because of the hair being thin, unclear, and similar in color to the entire image. When conducting image restoration tasks, the only available images are those obstructed by hair, and there is no corresponding ground truth (supervised data) of the same scene without hair obstruction. In addition, the texture information and structural information used in existing repair methods are often insufficient, which leads to poor results in skin cancer image repair. To address these challenges, we propose the intersection-union dual-stream cross-attention Lova-SwinUnet (IUDC-LS). Firstly, we propose the Lova-SwinUnet module, which embeds Lovasz loss function into Swin-Unet, enabling the network to better capture features of various scales, thus obtaining better hair mask segmentation results. Secondly, we design the intersection-union (IU) module, which takes the mask results obtained in the previous step for pairwise intersection or union, and then overlays the results on the skin cancer image without hair to generate the labeled training data. This turns the unsupervised image repair task into the supervised one. Finally, we propose the dual-stream cross-attention (DC) module, which makes texture information and structure information interact with each other, and then uses cross-attention to make the network pay attention to the more important texture information and structure information in the fusion process of texture information and structure information, so as to improve the effect of image repair. The experimental results show that the PSNR index and SSIM index of the proposed method are increased by 5.4875 and 0.0401 compared w

关键词： Image segmentation

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