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A more effective definition of evidential conflict combined with evidential distance

Journal of information and Computational Science 2011年第9期8卷 1729-1734页

作者： Chen, Yuxin Wu, Jiyi Cong, Jian Jie, Wenjing Deng, Yong School of Electronics and Information Technology Shanghai Jiao Tong University Shanghai 20024 China Hangzhou Key Lab of E-Business and Information Security Hangzhou Normal University Hangzhou 310036 China College of Food Science and Technology Shanghai Ocean University 999 Hucheng Huan Road Shanghai 201306 China Key Subject Laboratory of National Defense for Radioactive Waste and Environment Security Southwest University of Science and Technology Mianyang 621010 China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan Hunan 411105 China College of Computer and Information Sciences Southwest University Chongqing 400715 China

Evidence theory is widely used in many application systems such as information fusion and decision making under uncertainty environment. However, the classical Dempster combination rule is not efficient to deal with highly conflicting evidences. One of the open issues is how to determine the conflict degree in conflict evidence management. In this paper, a new conflict coefficient is proposed to measure the conflict degree between bodies of evidence. It can overcome some drawbacks of the classical conflict coefficient. The numerical example shows the efficiency of our proposed method. Copyright © 2011 Binary information Press.

关键词： information fusion

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Optimal aggregation of interval numbers based on genetic algorithm in group decision

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Journal of information and Computational Science 2011年第5期8卷 842-849页

作者： Kang, Bingyi Zhang, Yajuan Deng, Xinyang Wu, Jiyi Sun, Xiaohong Li, Ya Deng, Yong College of Computer and Information Sciences Southwest University Chongqing 400715 China Hangzhou Key Lab of E-Business and Information Security Hangzhou Normal University Hangzhou 310036 China College of Food Science and Technology Shanghai Ocean University 999 Hucheng Huan Road Shanghai 201306 China Key Subject Laboratory of National Defense for Radioactive Waste and Environmental Security Southwest University of Science and Technology Mianyang 621010 China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan Hunan 411105 China School of Electronics and Information Technology Shanghai Jiaotong University Shanghai 200240 China

A group of decision-makers may differ in their choice of alternatives while making a decision. As a result, how to aggregate opinions of different experts is still an open issue. Due to the uncertainty in the process of decision making, some mathematic tools such as fuzzy sets theory and evidence theory, are used to model uncertain information. In this paper, the experts' opinions are represented by interval numbers. A new optimal aggregation method to combine interval data is proposed based on genetic algorithm. The new method can deal with experts' opinions in an efficient manner. A numerical example on group decision making under uncertain environment is used to illustrate the efficiency of our proposed method. Copyright © 2011 Binary information Press.

关键词： Genetic algorithms

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Exploiting associations between word clusters and document classes for cross-domain text categorization

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Statistical Analysis and Data Mining 2011年第1期4卷 100-114页

作者： Zhuang, Fuzhen Luo, Ping Xiong, Hui He, Qing Xiong, Yuhong Shi, Zhongzhi The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100039 China Graduate University of Chinese Academy of Sciences Beijing 100039 China Hewlett Packard Labs China China MSIS Department Rutgers University United States Innovation Works United States

Cross-domain text categorization targets on adapting the knowledge learnt from a labeled source domain to an unlabeled target domain, where the documents from the source and target domains are drawn from different distributions. However, in spite of the different distributions in raw-word features, the associations between word clusters (conceptual features) and document classes may remain stable across different domains. In this paper, we exploit these unchanged associations as the bridge of knowledge transformation from the source domain to the target domain by the non-negative matrix tri-factorization. Specifically, we formulate a joint optimization framework of the two matrix tri-factorizations for the source- and target-domain data, respectively, in which the associations between word clusters and document classes are shared between them. Then, we give an iterative algorithm for this optimization and theoretically show its convergence. The comprehensive experiments show the effectiveness of this method. In particular, we show that the proposed method can deal with some difficult scenarios where baseline methods usually do not perform well. Copyright © 2010 Wiley Periodicals, Inc.

关键词： Iterative methods

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DH-TRIE frequent pattern mining on Hadoop using JPA

DH-TRIE frequent pattern mining on Hadoop using JPA

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IEEE International Conference on Granular Computing (GRC)

作者： Lai Yang Zhongzhi Shi Li D. Xu Fan Liang Ilan Kirsh Chinese Academy of Sciences Beijing China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Department of .ITDS Old Dominion University Norfolk VA USA The Academic College of Tel-Aviv Yafo Israel

The FPgrowth is a famous frequent pattern's algorithm in data mining when working with high-dimensional, large-scale data sets. It is also known as great complexity on memory for the recursively processing. In general, FPgrowth cannot handle large-scale data set unless dividing a whole data set into small blocks. Based on Hadoop, the open cloud computing model, a distributed DH-TRIE frequent pattern algorithm using JPA is proposed, which solved the three problems (globalization, random-write and duration). The algorithm is shown good flexibility and scalability by comparisons to mahout project. By applied to a virtualization platform Vega Cloud, the algorithm will be used in far-ranging situations.

关键词： Data mining Cloud computing Java Programming Data models Indexing

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CLUSTERING VIA DIMENSIONAL REDUCTION METHOD FOR THE PROJECTION PURSUIT BASED ON THE ICSA

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Journal of Electronics(China) 2010年第4期27卷 474-479页

作者： Gou Shuiping Feng Jing Jiao Licheng Key Laboratory of Intelligent Perception and Image Understanding for the Ministry of Education Institute of Intelligent Information Processing Xidian University Xi＇an 710071 China

The performance of the classical clustering algorithm is not always satisfied with the high-dimensional datasets, which make clustering method limited in many application. To solve this problem, clustering method with Projection Pursuit dimension reduction based on Immune Clonal Selection Algorithm (ICSA-PP) is proposed in this paper. Projection pursuit strategy can maintain consistent Euclidean distances between points in the low-dimensional embeddings where the ICSA is used to search optimizing projection direction. The proposed algorithm can converge quickly with less iteration to reduce dimension of some high-dimensional datasets, and in which space, K-mean clustering algorithm is used to partition the reduced data. The experiment results on UCI data show that the presented method can search quicker to optimize projection direction than Genetic Algorithm (GA) and it has better clustering results compared with traditional linear dimension reduction method for Principle Component Analysis (PCA).

关键词： Projection Pursuit (PP) Immune Clonal Selection Algorithm (ICSA) Genetic Algorithm (GA) K-means clustering

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一种基于模型共享的半监督多标签图像学习法

一种基于模型共享的半监督多标签图像学习法

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第十二届中国虚拟现实大会

作者： ZHANG Da-peng 张大鹏 WEN Jia 闻佳 LIU Xi 刘曦 Institute of Information Science and Engineering Yanshan University Qinhuangdao 066004 China Guan 燕山大学信息科学与工程学院秦皇岛 066004 桂林电子科技大学广西可信软件重点实验室桂林 541004 Institute of Information Science and Engineering Yanshan University Qinhuangdao 066004 China 燕山大学信息科学与工程学院秦皇岛 066004 The Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese 中国科学院计算技术研究所智能信息处理重点实验室北京 100190

提出一种快速且有效的半监督多标签学习方法：模型共享半监督推举。该方法能发现、共享并组合多个基模型，每个基模型是在某个标签上利用半监督支持向量机(S3VM)上学习的。通过使用模型共享，标签关联被显示地利用且对于每个标签来说只... 详细信息

提出一种快速且有效的半监督多标签学习方法：模型共享半监督推举。该方法能发现、共享并组合多个基模型，每个基模型是在某个标签上利用半监督支持向量机(S3VM)上学习的。通过使用模型共享，标签关联被显示地利用且对于每个标签来说只需要少量的基模型即可生成最后的决策结果。在Core15k和Mediamill数据集上评估方法，实验结果显示的方法与当前流行的监督和半监督多标签学习方法是可比的。

关键词：信息处理半监督多标签学习算法模型共享支持向量机

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Robust and discriminative image authentication based on sparse coding

Robust and discriminative image authentication based on spar...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Luntian Mou Tiejun Huang Yonghong Tian Shiguo Lian Xilin Chen Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China Institute of Digital Media Peking University Beijing China France Telecom Research and Development Center (Beijing) Beijing China

Image authentication is usually approached by checking the preservation of some invariant features, which are expected to be both robust and discriminative so that content-preserving operations are accepted while content-altering manipulations are rejected. However, most of existing features have not obtained convincing performance due to insufficiency of experiments and over biasing of robustness. Motivated by the sparse coding strategy discovered in primary visual cortex, we explore the possibility of using sparse coding coefficients for image authentication. Through extensive experiments, we discover that the proposed feature bears great discrimination as well as robustness, which indicates the effectiveness of sparse coding as a new invariant feature for image authentication.

关键词： Authentication Image coding Robustness Feature extraction Discrete cosine transforms Encoding Visualization

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imiRTP: An Integrated Method to Identifying miRNA-target Interactions in Arabidopsis thaliana

imiRTP: An Integrated Method to Identifying miRNA-target Int...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jiandong Ding Shusi Yu Uwe Ohler Jihong Guan Shuigeng Zhou Shanghai Key Laboratory of Intelligent Information Processing and School of Computer Science Fudan University Shanghai China Institute for Genome Sciences & Policy and Department of Biostatistics & Bioinformatics Duke University Durham NC USA Department of Computer Science & Technology Tongji University Shanghai China

MiRNA are about 22nt long small noncoding RNAs that post transcriptionally regulate gene expression in animals, plants and protozoa. Confident identification of MiRNA-Target Interactions (MTI) is vital to understand their function. Currently, several integrated programs and databases are available for animal miRNAs, the mechanisms of which are significantly different from plant miRNAs. Here we present imiRTP, an integrated MTI prediction and analysis toolkit for Arabidopsis thaliana. It features two important functions: (i) combination of several effective plant miRNA target prediction methods provides a sufficiently large MTI candidate set, and (ii) different filters allow for an efficient selection of potential targets. The modularity of imiRTP enables the prediction of high quality targets on genome-wide scale.

关键词： Bioinformatics RNA Animals Genomics Databases Prediction methods Educational institutions

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Determination of relative significance factor of impact categories based on fuzzy multiattribute decision-making method

Determination of relative significance factor of impact cate...

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Chinese Control and Decision Conference, CCDC

作者： Xinyang Deng Yajuan Zhang Bingyi Kang Jiyi Wu Xiaohong Sun Yong Deng School of Computer and Information Sciences Southwest University Chongqing China Hangzhou Key Laboratory of E-Business and Information Security Hangzhou Normal University Hangzhou Zhejiang China College of Food Science and Technology Shanghai Ocean University Shanghai China Laboratory of Intelligent Computing & Information Processing of Ministry of Education Xiangtan University Xiangtan Hunan China School of Electronics & Information Technology Shanghai Jiaotong University Shanghai China Laboratory of National defense for Radioactive Waste and Environmental Security Southwest University of Science and Technology Mianyang China

A relative significance factor of an impact category is the external weight of the impact category and plays an important role in life cycle assessment(LCA). Many multiattribute decision-making (MADM) methods such as analytical hierarchy process(AHP), the rank-order centroid method and the fuzzy method are proposed to determine the relative significance factor of an impact category. However, in previous research, it is shown that, the AHP approach is difficult for experts rendering consistent judgment when the number of pairwise comparisons increases, the rank-order method has some insurmountable shortcomings, the key issue to use fuzzy set theory is its complexity. In order to address this problem, a new simple and efficient method is proposed to determine relative significance factor in a more efficient manner. An example to determine relative significance factor of impact categories is shown to illustrate the efficiency of the proposed method.

关键词： Decision making Educational institutions Complexity theory Pragmatics Uncertainty Fuzzy set theory Fuzzy sets

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Adaptive discriminant analysis for face recognition from single sample per person

Adaptive discriminant analysis for face recognition from sin...

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International Conference on Automatic Face and Gesture Recognition

作者： Meina Kan Shiguang Shan Yu Su Xilin Chen Wen Gao Key Laboratory of Intelligent Information Processing Chinese Academy and Sciences Beijing China Chinese Academy of Sciences Beijing Beijing CN Department of Computer Science University of Caen France Institute of Digital Media Peking University Beijing China School of Computer Science and Technology Harbin Institute of Technology Beijing China

Discriminant analysis, especially Fisherface and its numerous variants, have achieved great success in face recognition. However, these methods fail to work for face recognition from Single Sample per Person (SSPP), since they need more than one sample per person to estimate the within-class scatter matrix. To break this inability of traditional discriminant analysis, our paper proposes Adaptive Discriminant Analysis (ADA). In our method, the within-class scatter matrix of each enrolled subject is estimated from his/her single sample, by inferring from a generic training set with multiple samples per person. The inference is inspired by a simple intuition that similar person follows similar within-class variations. Specifically, both kNN regression and Lasso regression are explored for this purpose. We evaluate our method on FERET database and a large real-world face database. The results are very impressive compared with dominant traditional solutions to SSPP problem.

关键词： Training Databases Face Face recognition Testing Lighting Nearest neighbor searches

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