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Ensemble feature selection integrating elitist roles and quantum game model

Journal of Systems Engineering and Electronics 2015年第3期26卷 584-594页

作者： Weiping Ding Jiandong Wang Zhijin Guan Quan Shi College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics School of Computer Science and Technology Nantong University Provincial Key Laboratory for Computer Information Processing Technology Soochow University

To accelerate the selection process of feature subsets in the rough set theory （RST）, an ensemble elitist roles based quantum game （EERQG） algorithm is proposed for feature selec- tion. Firstly, the multilevel elitist roles based dynamics equilibrium strategy is established, and both immigration and emigration of elitists are able to be self-adaptive to balance between exploration and exploitation for feature selection. Secondly, the utility matrix of trust margins is introduced to the model of multilevel elitist roles to enhance various elitist roles＇ performance of searching the optimal feature subsets, and the win-win utility solutions for feature selec- tion can be attained. Meanwhile, a novel ensemble quantum game strategy is designed as an intriguing exhibiting structure to perfect the dynamics equilibrium of multilevel elitist roles. Finally, the en- semble manner of multilevel elitist roles is employed to achieve the global minimal feature subset, which will greatly improve the fea- sibility and effectiveness. Experiment results show the proposed EERQG algorithm has superiority compared to the existing feature selection algorithms.

关键词： ensemble quantum game utility matrix of trust mar-gin dynamics equilibrium strategy multilevel elitist role feature selection and classification.

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A Novel User Behavior Prediction and Optimization Algorithm for Single-User Multi-terminal Scenario

A Novel User Behavior Prediction and Optimization Algorithm ...

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International Symposium on Computational Intelligence and Design, ISCID

作者： Hui Zhang Juan Chen Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China Jiangsu Key Lab of Wireless Communications Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781509035595

For the single-user multi-terminal scenario, a novel user service behavior prediction and optimization algorithm is proposed in this paper, which can realize the accurate prediction of service requirements and optimal selection of service terminals, so as to improve user QoE effectively. Firstly, by using an adaptive feedback based weight correction method, a modified entropy weighted Markov model is formulated to accurately predict user future service states. Secondly, according to the above prediction results, the corresponding heavy-tailed model is used to estimate the service duration. Moreover, by integrating service preferences and terminal attributes, a matching metric of services and terminals is established, and then a terminal selection model is proposed for recommending the optimal service terminal to each user. Simulation results show the effectiveness of proposed algorithm.

关键词： Predictive models Markov processes Optimization Entropy Adaptation models Prediction algorithms Measurement

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A novel image tag completion method based on convolutional neural transformation

arXiv

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arXiv 2017年

作者： Geng, Yanyan Zhang, Guohui Li, Weizhi Gu, Yi Liang, Ru-Ze Liang, Gaoyuan Wang, Jingbin Wu, Yanbin Patil, Nitin Wang, Jing-Yan Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China Huawei Technologies Co. Ltd. Shanghai China Suning Commerce R&D Center USA Inc. Palo AltoCA94304 United States Analytics & Research Travelers HartfordCT06183 United States King Abdullah University of Science and Technology Saudi Arabia Jiangsu University of Technology Jiangsu213001 China Information Technology Service Center Intermediate People’s Court of Linyi City Linyi China Hebei University of Economics and Business Shijiazhuang050061 China Savitribai Phule Pune University Pune Maharashtra411007 India

In the problems of image retrieval and annotation, complete textual tag lists of images play critical roles. However, in real-world applications, the image tags are usually incomplete, thus it is important to learn the complete tags for images. In this paper, we study the problem of image tag complete and proposed a novel method for this problem based on a popular image representation method, convolutional neural network (CNN). The method estimates the complete tags from the convolutional filtering outputs of images based on a linear predictor. The CNN parameters, linear predictor, and the complete tags are learned jointly by our method. We build a minimization problem to encourage the consistency between the complete tags and the available incomplete tags, reduce the estimation error, and reduce the model complexity. An iterative algorithm is developed to solve the minimization problem. Experiments over benchmark image data sets show its effectiveness. Copyright © 2017, The Authors. All rights reserved.

关键词： Convolution

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A hierarchical image matting model for blood vessel segmentation in fundus images

arXiv

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arXiv 2017年

作者： Fan, Zhun Lu, Jiewei Li, Wenji Wei, Caimin Huang, Han Cai, Xinye Chen, Xinjian Guangdong Provincial Key Laboratory of Digital Signal and Image Processing College of Engineering Shantou University Shan'tou515063 China Department of Mathematics Shantou University Shan'tou515063 China School of Software Engineering South China University of Technology Guang'zhou510006 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Jiang'su210016 China Medical Image Processing and Analysis Lab School of Electronics and Information Engineering Soochow University Su'zhou215006 China

In this paper, a hierarchical image matting model is proposed to extract blood vessels from fundus images. More specifically, a hierarchical strategy utilizing the continuity and extendibility of retinal blood vessels is integrated into the image matting model for blood vessel segmentation. Normally the matting models require the user specified trimap, which separates the input image into three regions manually: the foreground, background and unknown regions. However, since creating a user specified trimap is a tedious and time-consuming task, region features of blood vessels are used to generate the trimap automatically in this paper. The proposed model has low computational complexity and outperforms many other state-of-art supervised and unsupervised methods in terms of accuracy, which achieves a vessel segmentation accuracy of 96.0%, 95.7% and 95.1% in an average time of 10.72s, 15.74s and 50.71s on images from three publicly available fundus image datasets DRIVE, STARE, and CHASE_DB1, respectively. Copyright © 2017, The Authors. All rights reserved.

关键词： Blood vessels

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Consensus algorithms for biased labeling in crowdsourcing

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information Sciences 2017年 382-383卷 254-273页

作者： Jing Zhang Victor S. Sheng Qianmu Li Jian Wu Xindong Wu School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 P. R. China Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing 210094 P. R. China Department of Computer Science University of Central Arkansas Conway AR 72035 USA Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing 210044 P. R. China Institute of Intelligent Information Processing and Application Soochow University Suzhou 215006 P. R. China School of Computing and Informatics University of Louisiana at Lafayette LA 70504 USA

Although it has become an accepted lay view that when labeling objects through crowdsourcing systems, non-expert annotators often exhibit biases, this argument lacks sufficient evidential observation and systematic empirical study. This paper initially analyzes eight real-world datasets from different domains whose class labels were collected from crowdsourcing systems. Our analyses show that biased labeling is a systematic tendency for binary categorization; in other words, for a large number of annotators, their labeling qualities on the negative class (supposed to be the majority) are significantly greater than are those on the positive class (minority). Therefore, the paper empirically studies the performance of four existing EM-based consensus algorithms , DS, GLAD, RY, and ZenCrowd, on these datasets. Our investigation shows that all of these state-of-the-art algorithms ignore the potential bias characteristics of datasets and perform badly although they model the complexity of the systems. To address the issue of handling biased labeling, the paper further proposes a novel consensus algorithm, namely adaptive weighted majority voting (AWMV), based on the statistical difference between the labeling qualities of the two classes. AWMV utilizes the frequency of positive labels in the multiple noisy label set of each example to obtain a bias rate and then assigns weights derived from the bias rate to negative and positive labels. Comparison results among the five consensus algorithms (AWMV and the four existing) show that the proposed AWMV algorithm has the best overall performance. Finally, this paper notes some potential related topics for future study.

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Neighborhood collaborative classifiers

Neighborhood collaborative classifiers

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2016 International Conference on Machine Learning and Cybernetics, ICMLC 2016

作者： Xu, Suping Yang, Xibei Tsang, Eric C. C. Mantey, Eric A. School of Computer Science and Engineering Jiangsu University of Science and Technology Zhenjiang212003 China School of Economics and Management Nanjing University of Science and Technology Nanjing210094 China Faculty of Information Technology Macau University of Science and Technology C-Macau519020 China Intelligent Information Processing Key Laboratory of Shanxi Province Shanxi University Taiyuan030006 China Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province Zhejiang Ocean University Zhoushan316022 China

ISBN: (纸本)9781509003891

In neighborhood rough set model, the majority rule based neighborhood classifier (NC) is easy to be misjudged with the increasing of the size of information granules. To remedy this deficiency, we propose a neighborhood collaborative classifier (NCC) based on the idea of collaborative representation based classification (CRC). NCC first performs feature selection with neighborhood rough set, and then instead of solving the classification problem by the majority rule, NCC solves a similar problem with collaborative representation among the neighbors of each unseen sample. Experiments on UCI data sets demonstrate that: 1) Our NCC achieves satisfactory performance in larger neighborhood information granules when compared with NC;2) NCC reduces the size of dictionary when compared with CRC. © 2016 IEEE.

关键词： Classification (of information)

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Ranking Node Influence in Social Networks

Ranking Node Influence in Social Networks

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International Symposium on Parallel and Distributed Computing

作者： Zheyi Chen Yuli Liu Weiping Zhu Department of Computer Science National Laboratory for Information Science and Technology Beijing China College of Mathematics and Computer Science Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing Fuzhou China

ISBN: (纸本)9781509041534

In the study of social networks, analyzing node influence and identifying influential nodes in social networks is of great theoretical and practical significance. To effectively evaluate node influence, a novel concept of influence label is introduced, which can measure node influence through two label attributes called influence level and node degree. Then, a novel influence model called LDM (Level and Degree Model) is proposed. LDM updates the influence label of each node iteratively by the quality of neighbors and the number of neighbors. A gain function that conforms to power-law distribution is also introduced to further optimize the accuracy of LDM. In the experiment part, to prove the effectiveness of LDM, LDM is compared with other typical methods by employing IC model to simulate the information diffusion process on four real-world social networks. Experimental results show that LDM can rank node influence more accurately than other methods.

关键词： social networking (online) CYP51A1 gene LDM Social Networks Line of nodes Social Capital

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Towards multi-functional light-weight long-term real-time coastal ocean observation system 11

Towards multi-functional light-weight long-term real-time co...

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11th ACM International Conference on Underwater Networks and Systems, WUWNet 2016

作者： Jiang, Zhenfeng Ma, Yanming Chen, Jiali Wang, Zigeng Peng, Zheng Liu, Jun Han, Guitao Smart Ocean Industry Research Institute Nantong Jiangsu226000 China Computer Science and Engineering Department University of Connecticut StorrsCT06269 United States Computer Science Deparment City College of New York NY10031 United States Computer Science and Technology Department Jilin University Changchun Jilin130012 China State Key Laboratory of Robotics Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

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Robust monocular visual odometry using optical flows for mobile robots

Robust monocular visual odometry using optical flows for mob...

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Chinese Control Conference (CCC)

作者： Haifeng Li Zunhe Hu Xinwei Chen College of Computer Science and Technology Civil Aviation University of China Tianjin Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou Department of Computer Science Minjiang University Fuzhou

A robust optical flow-based visual odometry method using a single onboard camera is proposed in this paper. To improve the quality of the noisy optical flows, a correction method across multiple frames is developed. Furthermore, the optical flows in the plane at infinity are detected and removed as these optical flows have very low signal to noise ratio for robot translation estimation. Finally, a RANSAC approach for robot ego-motion estimation is proposed. Physical experiments are carried out and the results show that the proposed method is able to estimate the camera trajectory robustly with reasonable accuracy.

关键词： Adaptive optics Image motion analysis computer vision Optical imaging Optical sensors Cameras Optical noise

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Fractional-order control of USV course-keeping based on self-adaptive weight PSO algorithm

International Journal of Control and Automation

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International Journal of Control and Automation 2015年第10期8卷 309-320页

作者： Li, Guang-Yu Guo, Chen Li, Yan-Xin Deng, Wu Information Science and Technology College Dalian Maritime University Dalian China Software College Dalian Jiaotong University Dalian China Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis Guangxi University for Nationalities Nanning China The Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China

In order to improve the stability of underactuated surface vessels (USV) course-keeping, a fractional-order PIλDμcontroller based on self-adaptive weight particle swarm optimization (SAWPSO-FOCPID) was proposed for ship autopilot and had good overall control ability. The integral-order λ and differential-order μ can improve the controller with more robustness and take on stronger disturbance rejection ability. However, the difficulties for parameter verification are increased. A new designing method of PIλDμcontroller based on SAWPSO algorithm was proposed in order to balance the global search ability and local search ability of PSO algorithm. And at the same time, the difficulties of tuning more parameters and designing complexity of fractional-order PIλDμcontroller were solved. The SAWPSO-FOCPID controller was compared with the traditional PSO-PID controller under the same condition. The simulation results showed that the SAWPSO-FOCPID controller has a high dynamic tracking performance, small overshoot and strong immunity. © 2015 SERSC.

关键词： Controllers

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