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Immunodominance and clonal selection inspired multiobjective clustering

Progress in Natural Science:Materials International 2009年第6期19卷 751-758页

作者： Wenping Ma , Licheng Jiao, Maoguo Gong key laboratory of Intelligent Perception and image Understanding of the Ministry of Education of China, Institute of Intelligent Information processing, Xidian University, Xi’an 710071, China [a]Key Laboratory of Intelligent Perception and Image Understanding of the Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi’an 710071 China

The biological immune system is a highly parallel and distributed adaptive system. The information processing abilities of the immune system provide important insights into the field of computation. Based on immunodominance in the biological immune system and the clonal selection mechanism, a novel data mining method, Immune Dominance Clonal Multiobjective Clustering algorithm (IDCMC), is presented. The algorithm divides an individual population into three sub-populations according to three different measurements, and adopts different evolution and selection strategies for each sub-population. The update of each sub-population, however, is not carried out in isolation. The periodic combination operation of the analysis of the three sub-populations represents considerable advantages in its global search ability. The clustering task is a multiobjective optimization problem, which is more robust with respect to the variety of cluster structures of different datasets than a single-objective clustering algorithm. In addition, the new algorithm can determine the number of clusters automatically, which should identify the most promising clustering solutions in the candidate set. The experimental results, using artificial datasets with different manifold structure and handwritten digit datasets, show that the IDCMC outperforms the PESA-Ⅱ-based clustering method, the genetic algorithm-based clustering technique and the original K-Means algorithm in solving most of the problems tested.

关键词： Artificial immune systems Multiobjective optimization Clustering Unsupervised learning

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A novel quantum-inspired immune clonal algorithm with the evolutionary game approach

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Progress in Natural Science:Materials International 2009年第10期19卷 1341-1347页

作者： Qiuyi Wu,Licheng Jiao,Yangyang Li,Xiaozheng Deng key laboratory of Intelligent Perception and image Understanding of Ministry of Education of China,Institute of Intelligent Information processing,Xidian University,Xi’an 710071,China [a]Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi’an 710071 China

The quantum-inspired immune clonal algorithm(QICA) is a rising intelligence *** on evolutionary game theory and QICA,a quantum-inspired immune algorithm embedded with evolutionary game(EGQICA) is proposed to solve combination optimization *** this paper,we map the quantum antibody’s finding the optimal solution to player’s pursuing maximum utility by choosing strategies in evolutionary *** dynamics is used to model the behavior of the quantum antibody and the memory mechanism is also introduced in this *** results indicate that the proposed approach maintains a good diversity and achieves superior performance.

关键词： Game theory Evolutionary game Replicator dynamics Quantum-inspired optimization Artificial immune system

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New method in information processing for maintaining an efficient dynamic ordered set

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Science in China(Series F) 2009年第8期52卷 1292-1301页

作者： XIN ShiQing WANG GuoJin Institute of Computer Graphics and Image Processing Zhejiang University Hangzhou 310027 China State Key Laboratory of CAD & CG Zhejiang University Hangzhou 310027 China

This paper investigates how to maintain an efficient dynamic ordered set of bit strings, which is an important problem in the field of information search and information processing. Generally, a dynamic ordered set is required to support 5 essential operations including search, insertion, deletion, max-value retrieval and next-larger-value retrieval. Based on previous research fruits, we present an advanced data structure named rich binary tree （RBT）, which follows both the binary-search-tree property and the digital-search-tree property. Also, every key K keeps the most significant difference bit （MSDB） between itself and the next larger value among K＇s ancestors, as well as that between itself and the next smaller one among its ancestors. With the new data structure, we can maintain a dynamic ordered set in O（L） time. Since computers represent objects in binary mode, our method has a big potential in application. In fact, RBT can be viewed as a general-purpose data structure for problems concerning order, such as search, sorting and maintaining a priority queue. For example, when RBT is applied in sorting, we get a linear-time algorithm with regard to the key number and its performance is far better than quick-sort. What is more powerful than quick-sort is that RBT supports constant-time dynamic insertion/deletion.

关键词： information processing dynamic ordered set algorithms and data structures rich binary tree

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Improved rice algorithm of lossless compression for on-board images

Improved rice algorithm of lossless compression for on-board...

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International Conference on Multimedia Technology

作者： Wei, Yong-Wang Ding, Qing-Hai Luo, Hai-Bo Shenyang Institute of Automation Chinese Academy of Science Shenyang 110016 China Graduate School of Chinese Academy of Science Beijing 100049 China Key Laboratory of Optical-Electronics Information Processing Chinese Academy of Science Shenyang 110016 China Key Laboratory of Image Understanding and Computer Vision Liaoning Province 110016 China Electrical and Information Engineering College Beihua University Jilin 132021 China Research Institute of General Development and Demonstration of Equipment Equipment Academy of Air Force Beijing100076 China

ISBN: (纸本)9781424478743

Aiming at the problems of the relatively lower compression ratio and the difficulty in hardware implementation regarding Rice lossless compression algorithm for the Space-borne Remote-sensing images, an improved Rice method is proposed in which the length of predictor FIFO is determined by the size of input images and the global unicode for differences is used. The improved Rice algorithm is mainly composed of two steps, MED prediction for the whole row data and Rice global uniform entropy coding. The statistical results show that it can significantly reduce the spatial redundancy of the adjacent pixels and the mean value of image differences. The compression bit rate is reduced approximately 0.4581 bpp(bit/pixel) against the original Rice algorithm. Furthermore, the new way reserves the all detail information of the input images throughout the whole encoding process. Because we split the all samples with the same mode, the proportion of the identifier of the splitting mechanic in the output data stream is reduced and the compression ratio is increased. The improvement can reduce the encoding complexity and be easily implemented for hardware application. ©2010 IEEE.

关键词： Signal encoding

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image retrieval based on character-matching technology

Image retrieval based on character-matching technology

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5th International Conference on Information Assurance and Security, IAS 2009

作者： Shan, Zhao Jing, Liu School of Computer Science and Technology Henan Polytechnic University Jiaozuo 454003 China Jiangsu Province Key Lab. on Image Processing and Image Communication Nanjing University of Posts and Communication Nanjing 210003 China

ISBN: (纸本)9780769537443

In order to introduce effective text-retrieval technologies into content-based image retrieval, a novel method based on string-matching technology was presented incorporating the different sensitivity along variant directions of human visual system. The image is firstly divided into non-overlapped blocks and sub-feature is extracted according to the human visual feature. Then, a novel descriptor is defined. Based on which, the sub-features is mapping into characters and the image into the text. Finally, the technology of the character string matching is adopted as the similarity measurement. Meantime, the situation information of character is considered, an adjusted method is proposed. Experimental results show that the proposed method has sound and robust retrieval performance. © 2009 IEEE.

关键词： Content based retrieval

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image retrieval based on bit-plane distribution feature

Image retrieval based on bit-plane distribution feature

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

作者： Shan, Zhao Jing, Lui School of Computer Science and Technology Henan Polytechnic University Jiaozuo 454003 China Jiangsu Prov. Key Lab. on Image Processing and Image Communication Nanjing University of Posts and Communication Nanjing 210003 China

ISBN: (纸本)9780769537351

Based on the analysis of color histogram for image retrieval, a new descriptor, bit-plane distribution feature(BPDF), is proposed in this paper. The image is firstly divided into eight bit-planes. Meantime, the Gray code of bit-planes is used to avoid the effect of changes in the intensity values on bit-planes. Then, according to the distribution of each bit-plane, a feature vector is constructed by the first four significant planes which contain most of the structural information of the image. Finally, the Mahalanobis distance is adopted to measure the similarity because of the correlation between the concerned vectors after designing a correlation-weighted matrix. Comparisons are conducted between BPDF and other descriptors. Experimental results show that the proposed method provides more significantly retrieval results than the traditional ones. © 2009 IEEE.

关键词： image retrieval

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Center matching scheme for K-means cluster ensembles

Center matching scheme for K-means cluster ensembles

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MIPPR 2009 - Pattern Recognition and Computer Vision: 6th International Symposium on Multispectral image processing and Pattern Recognition

作者： Zhang, Li Zhou, Weida Wu, Caili Huo, Jieting Zou, Haishuang Jiao, Licheng Institute of Intelligent Information Processing Key Laboratory of Intelligent Perception and Image Understanding Xidian University 710071 China

ISBN: (纸本)9780819478078

In this paper, a center matching scheme is proposed for constructing a consensus function in the k-means cluster ensemble learning. Each k-means clusterer outputs a sequence with k cluster centers. We randomly select a cluster center sequence as a reference one, and then we rearrange the other cluster center sequences according to the reference sequence. Then we label the data using these matched cluster center sequences. Hence we get multiple partitions or clusterings. Finally, multiple clusterings are combined to the best labeling by using combination rules, such as the majority voting rule, the weighted voting rule and the selective weighted voting rule. Experimental results on 7 UCI data sets show that our ensemble methods could improve the clustering results effectively. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Clustering algorithms

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Pre-extracting boundary vectors for support vector machine using pseudo-density estimation method

Pre-extracting boundary vectors for support vector machine u...

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MIPPR 2009 - Pattern Recognition and Computer Vision: 6th International Symposium on Multispectral image processing and Pattern Recognition

作者： Zhang, Li Zhou, Weida Chen, Guirong Zhou, Hongjie Ye, Ning Jiao, Licheng Institute of Intelligent Information Processing Key Laboratory of Intelligent Perception and Image Understanding Xidian University 710071 China

ISBN: (纸本)9780819478078

Support vector machines (SVMs) have become useful and universal learning machines. SVMs construct a decision function by support vectors (SVs) and their corresponding weights. The training phase of SVMs definitely uses all training samples, which leads to a large computational complexity for a large scale sample set. Moreover support vectors could not be found until a quadratic programming (QP) problem is solved. Actually we know only SVs play a role in the decision function. Hence, pseudo density estimation (PDE) is presented to extract a set of boundary vectors (BVs) which may contain SVs. The PDE method is a variant of Parzen window method. Hyperspheres are considered as the window functions. In our method, for each sample we construct a hypersphere with an unfixed radius. The ratio of the number of samples contained in the hypersphere of a sample to the total training samples can be taken as the pseudo density of the corresponding sample. The set of BVs is taken as the training input to SVMs. In doing so, it speeds the training procedure of SVMs. It is convenient for PDE to determine its parameter. The experiments show that SVMs using PDE have the similar generalization performance to SVMs. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Support vector machines

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K-means selective cluster ensembles based on multiple feature subsets

K-means selective cluster ensembles based on multiple featur...

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MIPPR 2009 - Pattern Recognition and Computer Vision: 6th International Symposium on Multispectral image processing and Pattern Recognition

作者： Zhang, Li Zhou, Weida Zou, Haishuang Huo, Jieting Wu, Caili Jiao, Licheng Institute of Intelligent Information Processing Key Laboratory of Intelligent Perception and Image Understanding Xidian University 710071 China

ISBN: (纸本)9780819478078

Combining multiple clusterers is emerged as a powerful method for improving both the robustness and the stability of unsupervised classification solutions. In this paper, k-means selective cluster ensembles based on multiple feature subsets are proposed. In the ensemble, a random subset of features is used to train an individual k-means clusterer. In the final step, the selective weighted voting scheme is used for finding the best partition. The consensus function is constructed by relabeling all partitions of clusterers and finding the best partition. Experimental results on 4 UCI data sets show that our ensemble method can improve the clustering performance. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Set theory

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Effective usage of credit records promotes cooperation on weighted networks

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Physical Review E 2010年第3期81卷 036112-036112页

作者： Chao Zhai Hai-Tao Zhang Yang Zhao Michael Z. Q. Chen Zhi-Hai Rong Bing-Hong Wang Key Laboratory of Image Processing and Intelligent Control Department of Control Science & Engineering Huazhong University of Science and Technology Wuhan 430074 People’s Republic of China State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan 430074 People’s Republic of China Department of Modern Physics University of Science and Technology of China Hefei 230026 People’s Republic of China School of Automation Nanjing University of Science and Technology Nanjing 210094 People’s Republic of China Department of Mechanical Engineering The University of Hong Kong Pok Fu Lam Road Hong Kong Department of Automation Donghua University Shanghai 200051 People’s Republic of China

The cooperative behaviors of players on weighted networks are investigated by incorporation of trust mechanisms into a well-accepted game model, i.e., the networked prisoner’s dilemma game, afterwards some weight-updating schemes are designed according to the credit records. Despite the differences in network topologies and strategy updating protocols, a simple yet significant principle surfaces that, to promote the emergence of cooperation over abundant weighted networks, only the latest credit record of partners is required to be taken into consideration, whereas incorporating more previous records may even deteriorate the cooperation performance. To support such an appealing principle, we have investigated more deeply into the role of credit records so as to give a detailed explanation underlying it. The virtue of this work lies in providing insights into the effective usage of the currently available credit records.

关键词： Network topology

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