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检索条件"机构=Key Laboratory of Intelligent Information Perception and Processing Technology"

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An efficient data mining framework on Hadoop using Java persistence API

An efficient data mining framework on Hadoop using Java pers...

引用

10th IEEE International Conference on Computer and information technology, CIT-2010, 7th IEEE International Conference on Embedded Software and Systems, ICESS-2010, 10th IEEE Int. Conf. Scalable Computing and Communications, ScalCom-2010

作者： Lai, Yang Zhi, Shi Zhong Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University Chinese Academy of Sciences Beijing 100039 China

ISBN: (纸本)9780769541082

Data indexing is common in data mining when working with high-dimensional, large-scale data sets. Hadoop, a cloud computing project using the MapReduce framework in Java, has become of significant interest in distributed data mining. A feasible distributed data indexing algorithm is proposed for Hadoop data mining, based on ZSCORE binning and inverted indexing and on the Hadoop SequenceFile format. A data mining framework on Hadoop using the Java Persistence API (JPA) and MySQL Cluster is proposed. The framework is elaborated in the implementation of a decision tree algorithm on Hadoop. We compare the data indexing algorithm with Hadoop MapFile indexing, which performs a binary search, in a modest cloud environment. The results show the algorithm is more efficient than naïve MapFile indexing. We compare the JDBC and JPA implementations of the data mining framework. The performance shows the framework is efficient for data mining on Hadoop. © 2010 IEEE.

关键词： Data mining

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Analysis of mode characteristic of guided electromagnetic wave in mine tunnels

Analysis of mode characteristic of guided electromagnetic wa...

引用

International Conference on intelligent Computing and intelligent Systems

作者： Yao, Shan-Hua Wu, Xian-Liang School of Electric and Information Engineering Anhui University of Science and Technology Huainan China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei China

ISBN: (纸本)9781424465835

According to the surface impedance method, the equations of both monofilar mode and bifilar mode of guided electromagnetic waves in mine tunnels were presented, the distribution of synthesized electromagnetic field in tunnels was given by utilizing image approach. The attenuation constant of monofilar mode in a circular tunnel was solved. The simulation results show that propagation attenuation of monofilar mode of low frequency would increase as increasing of frequency. The skin effect and attenuation would be fearful as cable closing tunnel wall nearby, and the attenuation would be reduced if cable was located in tunnel locate. The field of binIar mode is concentrated in space between double cable and around area, the effects are stronger due to space between double cables than space between double cables and tunnel wall, in same time that attenuation of mononIar mode would be increasing with the decreasing of space between cables and tunnel wall. ©2010 IEEE.

关键词： Cables

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Voting conditional random fields for multi-label image classification

Voting conditional random fields for multi-label image class...

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2010 3rd International Congress on Image and Signal processing, CISP 2010

作者： Wang, Xishun Liu, Xi Shi, Zhiping Shi, Zhongzhi Sui, Hongjian Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University of Chinese Academy of Sciences Beijing 100049 China

ISBN: (纸本)9781424465149

In our real world, there usually exist several different objects in one image, which brings intractable challenges to the traditional pattern recognition methods to classify the images. In this paper, we introduce a Conditional Random Fields (CRFs) model to deal with the Multi-label Image Classification problem. Considering the correlations of the objects, a second-order CRFs is constructed to capture the semantic associations between labels. Different initial feature weights are set to introduce the voting techniques for a better performance. We evaluate our methods on MSRC dataset and demonstrate high precision, recall and F1 measure, showing that our method is competitive. ©2010 IEEE.

关键词： Image classification

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2010 IEEE 5th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2010

作者： Zhang, Zhujin Wang, Shuo Zhang, Xingyi Zhang, Zheng Department of Control Science and Engineering Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074 China School of Computer and Information Science University of South Australia Adelaide SA 5095 Australia Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei 230039 China

ISBN: (纸本)9781424464388

Randí et al. proposed a significant graphical representation for DNA sequences, which is very compact and avoids loss of information. In this paper, we build a fast algorithm for this graphical representation with time complexity O(n2), and find another important advantage in the representation: no degeneracy. Moreover, we propose a new method to do similarity analysis of DNA sequences based on the representation. The approach adopts four elements of covariance matrix as a descriptor, and is illustrated on the first exon of beta-globin genes from 11 different species. © 2010 IEEE.

关键词： DNA sequences

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Two improvement strategies for PSO

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作者： Dou, Quansheng Liu, Shasha Jiang, Ping Zhou, Xiuhua Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100080 China School of Information and Electronics Engineering Shandong Institute of Business and Technology Yantai 264005 China

ISBN: (纸本)3642163262

This paper proposed an improved particle swarm optimization algorithm (IPSO) to solve continuous function optimization problems. Two improvement strategies named "Vector correction strategy" and "Jump out of local optimum strategy" were employed in our improved algorithm. The algorithm was tested using 25 newly proposed benchmark instances in Congress on Evolutionary Computation 2005 (CEC2005). The experimental results show that the search efficiency and the ability of jumping out from the local optimum of the IPSO have been significantly improved, and the improvement strategies are effective. © 2010 IFIP.

关键词： Particle swarm optimization (PSO)

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D-LDA: A topic modeling approach without constraint generation for semi-defined classification

D-LDA: A topic modeling approach without constraint generati...

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10th IEEE International Conference on Data Mining, ICDM 2010

作者： Zhuang, Fuzhen Luo, Ping Shen, Zhiyong He, Qing Xiong, Yuhong Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS China Graduate University of Chinese Academy of Sciences China Hewlett Packard Labs China Innovation Works China

ISBN: (纸本)9780769542560

We study what we call semi-defined classification, which deals with the categorization tasks where the taxonomy of the data is not well defined in advance. It is motivated by the real-world applications, where the unlabeled data may also come from some other unknown classes besides the known classes for the labeled data. Given the unlabeled data, our goal is to not only identify the instances belonging to the known classes, but also cluster the remaining data into other meaningful groups. It differs from traditional semi-supervised clustering in the sense that in semi-supervised clustering the supervision knowledge is too far from being representative of a target classification, while in semi-defined classification the labeled data may be enough to supervise the learning on the known classes. In this paper we propose the model of Double-latent-layered LDA (D-LDA for short) for this problem. Compared with LDA with only one latent variable y for word topics, D-LDA contains another latent variable z for (known and unknown) document classes. With this double latent layers consisting of y and z and the dependency between them, D-LDA directly injects the class labels into z to supervise the exploiting of word topics in y. Thus, the semi-supervised learning in D-LDA does not need the generation of pairwise constraints, which is required in most of the previous semi-supervised clustering approaches. We present the experimental results on ten different data sets for semi-defined classification. Our results are either comparable to (on one data sets), or significantly better (on the other nine data set) than the six compared methods, including the state-of-the-art semi-supervised clustering methods. © 2010 IEEE.

关键词： Classification (of information)

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Unsupervised evolutionary clustering algorithm for mixed type data

Unsupervised evolutionary clustering algorithm for mixed typ...

引用

Congress on Evolutionary Computation

作者： Zhi Zheng Maoguo Gong Jingjing Ma Licheng Jiao Qiaodi Wu Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an China

In this paper, we propose a novel unsupervised evolutionary clustering algorithm for mixed type data, evolutionary k-prototype algorithm (EKP). As a partitional clustering algorithm, k-prototype (KP) algorithm is a well-known one for mixed type data. However, it is sensitive to initialization and converges to local optimum easily. Global searching ability is one of the most important advantages of evolutionary algorithm (EA), so an EA framework is introduced to help KP overcome its flaws. In this study, KP is applied as a local search strategy, and runs under the control of the EA framework. Experiments on synthetic and real-life datasets show that EKP is more robust and generates much better results than KP for mixed type data.

关键词： Clustering algorithms Prototypes Algorithm design and analysis Partitioning algorithms Classification algorithms Evolutionary computation Encoding

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Immunodomaince based clonal selection clustering algorithm

Immunodomaince based clonal selection clustering algorithm

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Congress on Evolutionary Computation

作者： Ruochen Liu Zhengchun Shen Licheng Jiao Wei Zhang Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an China

Based on clonal selection principle and the immunodominance theory, a new immune clustering algorithm, Immunodomaince based Clonal Selection Clustering Algorithm (ICSCA) is proposed in this paper. An immunodomaince operator is introduced to the clonal selection algorithm, which can realize on-line gaining prior knowledge and sharing information among different antibodies. The proposed method has been extensively compared with Fuzzy C-means (FCM), Genetic Algorithm based FCM (GAFCM) and Clonal Selection Algorithm based FCM (CSAFCM) over a test suit of several real life datasets and synthetic datasets. The result of experiment indicates the superiority of the ICSCA over FCM, GAFCM and CSAFCM on stability and reliability for its ability to avoid trapping in local optimum.

关键词： Clustering algorithms Variable speed drives Immune system Partitioning algorithms Algorithm design and analysis Accuracy Immunity testing

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A hybrid multiobjective immune algorithm with region preference for decision makers

A hybrid multiobjective immune algorithm with region prefere...

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Congress on Evolutionary Computation

作者： Licheng Jiao Wei Zhang Ruochen Liu Fang Liu Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an China

Recently, one of the main tools of decision maker (DM) preference incorporation in the multiobjective optimization (MOO) has been using reference points and achievement scalarizing functions (ASF). The core idea of these methods is converting the original multiobjective problem (MOP) into single objective problem by using ASF to find a single preferred point. However, many DMs not only interest in a single point but also a set of efficient points in their preferred region. In this paper, we introduce a hybrid multiobjective immune algorithm (HMIA) for DM. It combines the immune inspired algorithm and region preference based on a novel dominance concept called region-dominance without ASF. The new algorithm can let DMs flexibly decide the number of reference points and accurately determine the preferred region with its simple and effective interactive methods. To exemplify its advantages, simulated results of HMIA are shown with some well-known problems.

关键词： Delta modulation Neodymium Immune system Cloning Encoding Computational complexity

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Optimizing detector distribution in V-detector negative selection using a constrained multiobjective immune algorithm

Optimizing detector distribution in V-detector negative sele...

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Congress on Evolutionary Computation

作者： Fang Liu Maoguo Gong Jingjing Ma Licheng Jiao Wei Zhang Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an China

In this paper, a novel constrained multiobjective immune algorithm for optimizing detector distribution in V-detector negative selection is proposed. The theory of artificial immune system (AIS) and the spirit of population evolution are introduced to generate detectors. By combining the constraint handling technique and AIS-based multiobjective optimization, the algorithm is able to steadily maximize the anomaly coverage with little extra cost, which means the distribution with maximized coverage of the non-self space and minimized overlapping among detectors with fixed size will be well realized. Furthermore, the new approach is tested on some benchmark problems. The experimental results show that compared with some state-of-the-art methods, our algorithm can remarkably outperform them in terms of enhancing the detection rate by optimizing distribution without increasing the number of detectors.

关键词： Detectors Optimization Immune system Testing Shape Training data Aerospace electronics

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