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A Cascaded Co-evolutionary Model for Attribute Reduction and Classification Based on Coordinating Architecture with Bidirectional Elitist Optimization

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Chinese Journal of Electronics 2017年第1期26卷 13-21页

作者： DING Weiping WANG Jiandong LI Yuehua CHENG Xueyun School of Computer Science and Technology Nantong University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Provincial Key Laboratory for Computer Information Processing Technology Soochow University College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics

A cascaded co-evolutionary model for Attribute reduction and classification based on Coordinating architecture with bidirectional elitist optimization(ARC-CABEO) is proposed for the more practical applications. The regrouping and merging coordinating strategy of ordinary-elitist-role-based population is introduced to represent a more holistic cooperative co-evolutionary framework of different populations for attribute reduction. The master-slave-elitist-based subpopulations are constructed to coordinate the behaviors of different elitists, and meanwhile the elitist optimization vector with the strongest balancing between exploration and exploitation is selected out to expedite the bidirectional attribute co-evolutionary reduction process. In addition, two coupled coordinating architectures and the elitist optimization vector are tightly cascaded to perform the co-evolutionary classification of reduction subsets. Hence the preferring classification optimization goal can be achieved better. Some experimental results verify that the proposed ARC-CABEO model has the better feasibility and more superior classification accuracy on different UCI datasets, compared with representative algorithms.

关键词： Attribute reduction for classification Bidirectional elitist optimization Coordinating architecture Cascaded co-evolutionary model

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Extracting initial and reliable negative documents to enhance classification performance 1

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PAKDD 2006 International Workshop on knowledge Discovery in Life Science Literature, KDLL 2006

作者： Wang, Hui Zuo, Wanli Key Laboratory of Symbolic Computation and Knowledge Engineering Jilin University Ministry of Education Changchun 130012 China

ISBN: (数字)9783540328100

ISBN: (纸本)3540328092

Most existing text classification work assumes that training data are completely labeled. In real life, some information retrieval problems can only be described as learning a binary classifier from a set of incompletely labeled examples, where a small set of labeled positive examples and a very large set of unlabeled ones are provided. In this case, all of the traditional text classification methods can't work properly. In this paper, we propose a method called Weighted Voting Classifier, which is an improved 1-DNF algorithm. Experimental results on the Reuters-21578 set show that our algorithm Weighting Voting Classifier outperforms PEBL and one-class SVM in terms of F measure. Weighting Voting Classifier can achieve high F score when comparing with PEBL and one-class SVM. Furthermore, the reduction of iterations is 2.26 when comparing the method of PEBL with ours. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Information retrieval

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Optimal control approach to stability criteria on Hill's equations

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Applied Mathematics(A Journal of Chinese Universities) 2018年第3期33卷 253-273页

作者： ZU Jian LI Yong School of Mathematics and Statistics & Center for Mathematics and Interdisciplinary Sciences North-east Normal University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University College of Mathematics Jilin University

This paper gives a tutorial on how to prove Lyapunov type criteria by optimal control methods. Firstly, we consider stability criteria on Hill’s equations with nonnegative potential. By optimal control methods developed in 1990s, we obtain several stability criteria including Lyapunov’s criterion, Neǐgauz and Lidskiǐ’s criterion. Secondly, we present stability criteria on Hill’s equations with sign-changing potential in which Brog’s criterion and Krein’s criterion are included.

关键词： stability criteria optimal control method Hill's equation Lyapunov inequality

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Experience Replay for Least-Squares Policy Iteration

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IEEE/CAA Journal of Automatica Sinica 2014年第3期1卷 274-281页

作者： Quan Liu Xin Zhou Fei Zhu Qiming Fu Yuchen Fu the School of Computer Science and Technology Soochow University the Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University the School of Computer Science and Technology Soochow University

Policy iteration,which evaluates and improves the control policy iteratively,is a reinforcement learning *** evaluation with the least-squares method can draw more useful information from the empirical data and therefore improve the data ***,most existing online least-squares policy iteration methods only use each sample just once,resulting in the low utilization *** the goal of improving the utilization efficiency,we propose an experience replay for least-squares policy iteration(ERLSPI)and prove its *** method combines online least-squares policy iteration method with experience replay,stores the samples which are generated online,and reuses these samples with least-squares method to update the control *** apply the ERLSPI method for the inverted pendulum system,a typical benchmark *** experimental results show that the method can effectively take advantage of the previous experience and knowledge,improve the empirical utilization efficiency,and accelerate the convergence speed.

关键词： Learning (artificial intelligence) Markov processes computational efficiency Convergence Least squares methods

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Computing minimal diagnosis with binary decision diagrams algorithm

Computing minimal diagnosis with binary decision diagrams al...

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6th International Conference on Fuzzy Systems and knowledge Discovery, FSKD 2009

作者： Wang, Kun Li, Zhan-Shan Ai, Yang Zhang, Yong-Gang Key Laboratory of Symbolic Computation Knowledge Engineering for Ministry of Education Jilin University Jilin Changchun China

ISBN: (纸本)9780769537351

In this paper we propose an algorithm of computing minimal diagnosis based on BDD (Binary Decision Diagram). First we give the concept of disjunction equations, and map the collection of conflict sets into disjunction equations, then we compile the system into a BDD, further we compute the minimal hitting sets by solving the BDD and minimizing hitting sets. The reliability and completeness of the algorithm are proved. Variable ordering and construct controlling of BDD are used to further reduce the size of final BDD and the number of non-minimal hitting sets to be generated. The efficiency of computing minimal hitting sets is highly improved. Experimental results show that the algorithm isn't restricted by the structure of system and conflict sets, compared with three algorithms in the references, it can generate all the minimal hitting sets, and it works well in random circumstances. © 2009 IEEE.

关键词： Binary decision diagrams

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Genetic algorithm with local search for advanced planning and scheduling

Genetic algorithm with local search for advanced planning an...

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3rd International Conference on Natural computation, ICNC 2007

作者： Pu, Yan Dayou, Liu Donghui, Yuan Ji, Yu Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun 130012 China

ISBN: (纸本)0769528759

In this paper, a genetic algorithm approach with a novel mutation operator based on perturbation and local search has been proposed to solve an advanced planning and scheduling (APS) model in manufacturing supply chain, in which each customer order has a due date, each operation could be performed on alternative machines. The objective is to minimize the makespan of each customer order while ensuring the due date constraints. Various sizes of numerical experiments were carried out to demonstrate the efficiency of the proposed GA and the results indicate that the presented algorithm performs much better than previous work especially in large size problems. © 2007 IEEE.

关键词： Genetic algorithms

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Complex knowledge system modeling based on Hierarchical Fuzzy Petri Net

Complex knowledge system modeling based on Hierarchical Fuzz...

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2007 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2007

作者： Hongjun, Pan Jigui, Sun Key Laboratory of Symbolic Computation and Knowledge Engineering Jilin University Ministry of Education Changchun 130012 China

ISBN: (纸本)0769530281

The difficulties of modeling complex knowledge system lie in a large quantity of knowledge rules and the difficulty in organizing rules and grasping their mutual logical relationships. This article proposed a concept of hierarchical fuzzy Petri nets (HFPN), which is more suitable for modeling complex knowledge system than other fuzzy Petri Net models. In addition, the concepts of abstract place and abstract transition in HFPN are allowed to describe and analyze the knowledge system at diverse abstract levels. Therefore, using HFPN, the iterative and incremental methods can be applied to modeling complex knowledge system. In addition, structured approach can be naturally applied to the process of modeling knowledge system. © 2007 IEEE.

关键词： Petri nets

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Multiparticipant Federated Feature Selection Algorithm With Particle Swarm Optimization for Imbalanced Data Under Privacy Protection

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第5期4卷 1002-1016页

作者： Hu, Ying Zhang, Yong Gong, Dunwei Sun, Xiaoyan China University of Mining and Technology School of Information and Control Engineering Xuzhou221008 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun130012 China

In the real world, data describing the same learning task may be distributed in different institutions (called participants), and these participants cannot share their own data due to the need of privacy protection. How to select an optimal feature subset on the basis of ensuring the data privacy of each participant is an important and challenging topic. At the same time, data on part participants show imbalanced or even miss some classes, further increasing the difficulty of solving this kind of problem. In view of this, by introducing a trusted third party to process and integrate optimal information from participants, this article proposes a multiparticipant federated evolutionary feature selection algorithm for imbalanced data under privacy protection. First, a multilevel joint sample filling strategy with multiple participants (called sampling-rough selection-fine tuning strategy) is proposed to fill imbalanced or empty classes on each participant while ensuring data privacy. Then, a federated evolutionary feature selection method based on particle swarm optimization (PSO) is proposed by periodically sharing the optimal feature subsets obtained by PSOs on participants. Finally, the proposed algorithm is applied to 15 test datasets, and compared with several typical imbalanced feature selection algorithms and two kinds of ensemble feature selection algorithms. The experimental results show that the proposed algorithm can significantly promote the ability of each participant on processing imbalanced datasets, and improve the classification accuracy of obtained feature subsets while protecting the participants' data privacy. © 2020 IEEE.

关键词： Clustering algorithms

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A Novel Iterative Velocity Control Algorithm and Its FPGA Implementation Based on Trigonometric Function

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Chinese Journal of Electronics 2019年第2期28卷 237-245页

作者： WEN Quangang LIANG Yanchun Adriano Tavares Ricardo Teixeira WU Chunguo Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Zhuhai College of Jilin University Department of Industrial Electronics University of Minho Campus de Azurm

The servo motor’s flexible acceleration/deceleration(acc/dec) control is an emerging research topic in the automation field. A velocity control algorithm based on trigonometric function is proposed in this *** required parameters, it transforms trigonometric calculations into elementary mathematical operations and calculates the velocity controlling values iteratively, which could avoid trigonometric calculation and reduce the computation time. Experimental results show that the proposed algorithm is suitable for implementation on field programmable gate arrays and achieves a flexible controlling, enhancing both the equipment’s stability and reliability. It is promising to significantly improve the high-speed computerized numerical control equipment’s controlling accuracy, without a huge hardware resource consumption.

关键词： Acceleration Velocity control Algorithm design and analysis Field programmable gate arrays Servomotors

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L-priorities Bloom Filter: A New Member of the Bloom Filter Family

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International Journal of Automation and computing 2012年第2期9卷 171-176页

作者： Huang-Shui Hu Hong-Wei Zhao Fei Mi College of Computer Science and Technology Jilin University Changchun 130012 PRC Key Laboratory of Symbolic Computation and Knowledge Engineering for Ministry of Education Jilin University Changchun 130012 PRC

A Bloom filter is a space-efficient data structure used for concisely representing a set as well as membership queries at the expense of introducing false positive. In this paper, we propose the L-priorities Bloom filter （LPBF） as a new member of the Bloom filter （BF） family, it uses a limited multidimensional bit space matrix to replace the bit vector of standard bloom filters in order to support different priorities for the elements of a set. We demonstrate the time and space complexity, especially the false positive rate of LPBF. Furthermore, we also present a detailed practical evaluation of the false positive rate achieved by LPBF. The results show that LPBF performs better than standard BFs with respect to false positive rate.

关键词： Bloom filter bit space matrix false positive L-priorities time and space complexity.

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