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Global well-posedness and decay estimates for the 2D chemotaxis model with the supercritical case

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Journal of Mathematical Analysis and Applications 2025年第1期552卷

作者： Peiyi Liu Qian Zhang Chinese Academy of Inspection & Quarantine Greater Bayarea Zhongshan 528000 PR China Hebei Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Information Science Hebei University Baoding 071002 PR China

In this paper, we investigate the Cauchy problem for the 2D chemotaxis model. By considering an external force term of the form − ρ q for 1 ≤ q < 2 , we establish the existence of a global classical solution and ...

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A hybrid monotone decision tree model for interval-valued attributes

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Advances in computational intelligence 2021年第1期2卷 1-11页

作者： Chen, Jiankai Li, Zhongyan Wang, Xin Zhai, Junhai School of Control and Computer Engineering North China Electric Power University Beijing China Hebei Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Information Science Hebei University Baoding China School of Mathematics and Physics North China Electric Power University Beijing China

The existing monotonic decision tree algorithms are based on a linearly ordered constraint that certain attributes are monotonously consistent with the decision, which could be called monotonic attributes, whereas others, called non-monotonic attributes. In practice, monotonic and non-monotonic attributes coexist in most classification tasks, and some attribute values are even evaluated as interval numbers. In this paper, we proposed a fuzzy rank-inconsistent rate based on probability degree to judge the monotonicity of interval numbers. Furthermore, we devised a hybrid model composed of monotonic and non-monotonic attributes to construct a mixed monotone decision tree for interval-valued data. Experiments on artificial and real-world data sets show that the proposed hybrid model is effective.

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A three-stage method for classification of binary imbalanced big data 19

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A three-stage method for classification of binary imbalanced...

19th International Conference on machine learning and Cybernetics, ICMLC 2020

作者： Zhai, Jun-Hai Zhang, Su-Fang Wang, Mo-Han Li, Yan Hebei University Hebei Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Information Science Baoding071002 China China Meteorological Administration Hebei Branch of China Meteorological Administration Training Center Baoding071000 China Beijing Normal University School of Applied Mathematics Zhuhai519087 China

ISBN: (纸本)9780738124261

In the real world, there are many imbalanced data classification problems, such as extreme weather prediction, software defect prediction, machinery fault diagnosis, spam filtering, etc. It has important theoretical and practical value to study the problem of imbalanced data classification. In the framework of binary imbalanced data classification, a three-stage method for classification of binary imbalanced big data was proposed in this paper. Specifically, in the first stage, the negative class big data was clustered into K clusters by K-means algorithm on Hadoop platform. In the second stage, we use instance selection method to select important samples from each cluster in parallel, and obtain K negative class subsets. In the third stage, we first construct K balanced training sets which consist of negative class subset and positive class subset, and then train K classifiers, and finally we integrate these classifiers to classify the unseen samples. Some experiments are conducted to compare the proposed method with two state-of-the-art methods on G-means. The experimental results demonstrate that the proposed method is more effective and efficient than the compared approaches. © 2020 IEEE.

关键词： Big data

Feature selection for big data based on mapreduce and voting mechanism 19

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Feature selection for big data based on mapreduce and voting...

19th International Conference on machine learning and Cybernetics, ICMLC 2020

作者： Zhang, Su-Fang Zhai, Jun-Hai Tian, Shi Zhou, Xiang Li, Yan China Meteorological Administration Hebei Branch of China Meteorological Administration Training Center Baoding071000 China Hebei University Hebei Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Information Science Baoding071002 China Beijing Normal University School of Applied Mathematics Zhuhai519087 China

ISBN: (纸本)9780738124261

With the rapid development of computer network technology and wireless sensor technology, as well as the arrival of the era of big data, the dimension and sample number of data are growing rapidly. Accordingly, it is important to investigate the problem of feature selection for big data and to design feature selection algorithm for big data. Based on MapReduce and voting mechanism, a feature selection method for big data is proposed in this paper. The proposed methods include three steps: Firstly, partition big data set into m subsets, and deploy the subsets to m computing nodes of Hadoop. Secondly, on the m computing nodes, we employ a feature selection algorithm based on genetic algorithm to select important features in parallel using local data subset, and obtain m feature subsets. Finally, for each feature, m feature subsets are used to vote on it, and the final feature subset is selected according to the voting results. Experimental results on four big data sets demonstrate that the proposed method is effective and efficient. © 2020 IEEE.

关键词： Genetic algorithms

HOM 3-LIE-RINEHART ALGEBRAS

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arXiv 2020年

作者： Bai, Ruipu Li, Xiaojuan Wu, Yingli College of Mathematics and Information Science Hebei University Key Laboratory of Machine Learning and Computational Intelligence of Hebei Province BaodingP.R.071002 China College of Mathematics and Information Science Hebei University Baoding071002 China

After endowing with a 3-Lie-Rinehart structure on Hom 3-Lie algebras, we obtain a class of special Hom 3-Lie algebras, which have close relationships with representations of commutative associative algebras. We provide a special class of Hom 3-Lie-Rinehart algebras, called split regular Hom 3-Lie-Rinehart algebras, and we then characterize their structures by means of root systems and weight systems associated to a splitting Cartan subalgebra. Copyright © 2020, The Authors. All rights reserved.

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A node deployment model with variable transmission distance for wireless sensor networks

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arXiv 2020年

作者： Tiegang, Fan Junmin, Chen Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Information Science Hebei University BaodingHebei071002 China College of Mathematics and Information Science Hebei University BaodingHebei071002 China

The deployment of network nodes is essential to ensure the wireless sensor network's regular operation and affects the multiple network performance metrics, such as connectivity, coverage, lifetime, and cost. This paper focuses on the problem of minimizing network costs while meeting network requirements, and proposes a corona-based deployment method by using the variable transmission distance sensor. Based on the analysis of node energy consumption and network cost, an optimization model to minimize Cost Per Unit Area is given. The transmission distances and initial energy of the sensors are obtained by solving the model. The optimization model is improved to ensure the energy consumption balance of nodes in the same corona. Based on these parameters, the process of network node deployment is given. Deploying the network through this method will greatly reduce network costs. Copyright © 2020, The Authors. All rights reserved.

关键词： Energy utilization

Research on location and similar comments in point-of-interest recommendation system for users

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Journal of Physics: Conference Series 2021年第1期1774卷

作者： Yu Liu Ning Wei College of Mathematics and Information Science Hebei University Baoding Hebei 071002 Hebei Key Laboratory of Machine Learning and Computational Intelligence

By combining deep learning algorithms and potential probabilistic topic models, this paper proposes a new modeling strategy for individual users and constructs an effective point of interest recommendation system. First, Doc2vec is used to vectorize the user's comments, and the Bow model is used to match the similarity of the points of interest, and then the potential probability model is used to cluster the positions of the points of interest. Finally, relying on the user's historical comment data as a basis, the Top-K ranking recommendation scheme is obtained. The experimental results show that for large-scale location-based social network data sets, the algorithm in this paper has an ideal final effect on users' personalized recommendations. The recommendation model for user location information and similar comment points of interest proposed in this paper is more subjectively and objectively in line with user behavior needs. On the premise of ensuring the real-time performance of recommendation, the model proposed in this paper effectively improves the accuracy of recommendation compared with traditional point-of-interest recommendation algorithms.

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The infinite dimensional unital 3-lie poisson algebra

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arXiv 2019年

作者： Kang, Chuangchuang Bai, Ruipu Wu, Yingli College of Mathematics and Information Science Hebei University Baoding071002 College of Mathematics and Information Science Hebei University Key Laboratory of Machine Learning and Computational Intelligence of Hebei Province Baoding071002

From a commutative associative algebra A, the infinite dimensional unital 3-Lie Poisson algebra L is constructed, which is also a canonical Nambu 3-Lie algebra, and the structure of is discussed. It is proved that: (1) there is a minimal set of generators S consisting of six vectors;(2) the quotient algebra L00,0is a simple 3-Lie Poisson algebra;(3) four important infinite dimensional 3-Lie algebras: 3-Virasoro-Witt algebra W3, AI, AIand the 3-Wazalgebra can be embedded in L. Copyright © 2019, The Authors. All rights reserved.

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3-Lie algebra Aδω-Modules and induced modules

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arXiv 2019年

作者： Bai, Ruipu Ma, Yue Liu, Pei College of Mathematics and Information Science Hebei University Key Laboratory of Machine Learning and Computational Intelligence of Hebei Province Baoding071002 College of Mathematics and Information Science Hebei University Baoding071002 China

In this paper, we define the induced modules of Lie algebra ad(B) associated with a 3-Lie algebra B-module, and study the relation between 3-Lie algebra Aδω-modules and induced modules of inner derivation algebra ad(Aδω). We construct two infinite dimensional intermediate series modules of 3-Lie algebra Aδω, and two infinite dimensional modules (V, ψ λμ) and (V, φμ) of the Lie algebra ad(Aδω), and prove that only (V, ψλ0) and (V, ψλ1) are induced modules. Copyright © 2019, The Authors. All rights reserved.

关键词： Algebra