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Scientific World Journal 2015年第1期2015卷 471371页

作者： Nguyen, Thanh-Tung Huang, Joshua Zhexue Nguyen, Thuy Thi Shenzhen Key Laboratory of High Performance Data Mining Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China University of Chinese Academy of Sciences Beijing 100049 China School of Computer Science and Engineering Water Resources University Hanoi 10000 Viet Nam College of Computer Science and Software Engineering Shenzhen University Shenzhen 518060 China Faculty of Information Technology Vietnam National University of Agriculture Hanoi 10000 Viet Nam

Random forests (RFs) have been widely used as a powerful classification method. However, with the randomization in both bagging samples and feature selection, the trees in the forest tend to select uninformative features for node splitting. This makes RFs have poor accuracy when working with high-dimensional data. Besides that, RFs have bias in the feature selection process where multivalued features are favored. Aiming at debiasing feature selection in RFs, we propose a new RF algorithm, called xRF, to select good features in learning RFs for high-dimensional data. We first remove the uninformative features using p-value assessment, and the subset of unbiased features is then selected based on some statistical measures. This feature subset is then partitioned into two subsets. A feature weighting sampling technique is used to sample features from these two subsets for building trees. This approach enables one to generate more accurate trees, while allowing one to reduce dimensionality and the amount of data needed for learning RFs. An extensive set of experiments has been conducted on 47 high-dimensional real-world datasets including image datasets. The experimental results have shown that RFs with the proposed approach outperformed the existing random forests in increasing the accuracy and the AUC measures. © 2015 Thanh-Tung Nguyen et al.

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An Effective Automatic XML Query Expansion Method Based on Local Co-occurrence Model for Pseudo-Relevance Feedback

An Effective Automatic XML Query Expansion Method Based on L...

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2012 Third International Conference on Theoretical and Mathematical Foundations of computer Science(ICTMF 2012)

作者： Minjuan Zhong School of Information Technology Jiangxi University of Finance and Economics Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics

Pseudo-relevance feedback has been perceived as an effective solution for automatic query ***,a recent study has shown that traditional pseudo-relevance feedback may bring into topic drift and hence be harmful to the retrieval *** this paper,using the idea of local analysis,an effective XML query expansion method,is presented,which utilizes the local word co-occurrence information in good pseudo-relevance document collection and the structural semantics of XML to select most appropriate expansion *** results on INEX 2005 IEEE-CS collection show that the proposed expansion method offers better retrieval performance,compared with original query.

关键词： Query Expansion,Pseudo-Relevance Feedback,information Retrieval,Local Word Co-occurrence

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Automatic data distribution in large-scale OLTP applications

International Journal of Database Theory and Application

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International Journal of database Theory and Application 2014年第4期7卷 37-46页

作者： Wang, Xiaoyan Fan, Xu Chen, Jinchuan Du, Xiaoyong Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China MOE Beijing 100872 China School of Information and Electrical Engineering Ludong University Yantai 264025 China School of Information Renmin University of China Beijing 100872 China

With the dramatic increase of data volume, automatic data distribution has been one of the key techniques and intractable problem for distributed systems. This work summarizes the problem of data distribution and abstracts it as a general triangle model called DaWN. Based on data and workload analysis, it presents a novel strategy called ADDS for automatic data distribution in OLTP applications. According to the results of a series of experiments over TPC-C datasets and transactions, the proposed approach shows effective improvements.© 2014 SERSC.

关键词： data handling

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Application and research of a BP neural network based on clustering

Application and research of a BP neural network based on clu...

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2014 International Conference on Artificial Intelligence and Industrial Application (AIIA2014)

作者： H.Wang Y.Gao A.L.Wulamu Y.H.Xie K.Liu Information Center of Anshan Iron and Steel Group Mining Company School of Computer and Communication Engineering University and Technology Beijing Beijing Key Laboratory of Knowledge Engineering for Materials Science

ISBN: (纸本)9781784660529

A BP neural network has a highly nonlinear mapping ability. It has been successfully used to predict many aspects of the steel industry. This paper introduces a working principle of the BP algorithm and uses the BP network to establish a predictive model of process parameters through inputting chemical element content and mechanical properties of steel, respectively using the predictive model to predict on original data and data after clustering according to certain rules. Then to compare the prediction results of process parameters before and after clustering with actual value. A number of experiments show that using a BP neural network to predict can more accurately reflect the actual situation after clustering.

关键词： BP neural network clustering prediction

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Analyzing information Needs of Elderly People: A Survey in Chinese Rural Community

Analyzing Information Needs of Elderly People: A Survey in C...

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2014年管理与服务科学国际学术会议

作者： Chunfang Zou Ping Zhou School of business administration Jiangxi University of Finance and Economics School of Information Technology Jiangxi University of Finance and Economics Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics

A survey about the information needs of elderly people could find out the information required to address the needs of the aged in a community. Analyzing data collected from 600 elderly people through

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Improved head-driven statistical models for natural language parsing

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Journal of Central South University 2013年第10期20卷 2747-2752页

作者：袁里驰 School of Information Technology Jiangxi University of Finance and Economics Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics

Head-driven statistical models for natural language parsing are the most representative lexicalized syntactic parsing models, but they only utilize semantic dependency between words, and do not incorporate other semantic information such as semantic collocation and semantic category. Some improvements on this distinctive parser are presented. Firstly, "valency" is an essential semantic feature of words. Once the valency of word is determined, the collocation of the word is clear, and the sentence structure can be directly derived. Thus, a syntactic parsing model combining valence structure with semantic dependency is purposed on the base of head-driven statistical syntactic parsing models. Secondly, semantic role labeling(SRL) is very necessary for deep natural language processing. An integrated parsing approach is proposed to integrate semantic parsing into the syntactic parsing process. Experiments are conducted for the refined statistical parser. The results show that 87.12% precision and 85.04% recall are obtained, and F measure is improved by 5.68% compared with the head-driven parsing model introduced by Collins.

关键词： valence structure semantic dependency head-driven statistical syntactic parsing semantic role labeling

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A Flexible and Efficient Algorithm for Regularized Marginal Fisher Analysis

A Flexible and Efficient Algorithm for Regularized Marginal ...

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International Joint Conference on Neural Networks

作者： Jinrong He Lixin Ding Lei Jiang Li Huang The School of Computer State Key Laboratory of Software Engineering Wuhan University Wuhan China State Key Laboratory of Software Engineering Wuhan University Wuhan China Key Laboratory of Knowledge Processing and Networked Manufacture Hunan University of Science and Technology Xiangtan China The School of Computer & Information Technology Shanxi University Shanxi China

Marginal Fisher analysis (MFA) is a well-known linear dimensionality reduction method. However, MFA does not utilize the local diversity information of the training data, which will degrade its performance. In order to enhance the discriminant power of MFA, this paper considers introducing local variation quantity to enlarge the distances between local neighborhood embeddings and proposes a flexible and efficient implementation of MFA (F-MFA) within the regularization framework. Therefore, the discriminant structure and diversity of data are preserved in low-dimensional subspace. Computationally, F-MFA is formulated as a trace differential optimization problem which can completely avoids the singularity problem as it exists in MFA. Further, an efficient algorithm is developed for implementing F-MFA via QR-decomposition. Experimental results on four face data sets demonstrate the effectiveness of our approach.

关键词： Algorithm design and analysis Symmetric matrices Linear programming Manifolds Eigenvalues and eigenfunctions Educational institutions TV

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Keeping speed and distance for aligned motion

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Physical Review E 2015年第1期91卷 012807-012807页

作者： Illés J. Farkas Jeromos Kun Yi Jin (金祎) Gaoqi He (何高奇) Mingliang Xu (徐明亮) MTA-ELTE Statistical and Biological Physics Research Group (Hungarian Academy of Sciences) Pázmány Péter sétány 1A Budapest 1117 Hungary Regional Knowledge Center ELTE Faculty of Sciences Irányi Dániel u. 4. Székesfehérvár 8000 Hungary Department of Biological Physics Eötvös University Pázmány Péter sétány 1A Budapest 1117 Hungary Department of Computer Science and Engineering East China University of Science and Technology 130 Meilong Road Shanghai 200237 China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing 100191 China School of Information Engineering Zhengzhou University 100 Science Avenue Zhengzhou 450001 China

The cohesive collective motion (flocking, swarming) of autonomous agents is ubiquitously observed and exploited in both natural and man-made settings, thus, minimal models for its description are essential. In a model with continuous space and time we find that if two particles arrive symmetrically in a plane at a large angle, then (i) radial repulsion and (ii) linear self-propelling toward a fixed preferred speed are sufficient for them to depart at a smaller angle. For this local gain of momentum explicit velocity alignment is not necessary, nor are adhesion or attraction, inelasticity or anisotropy of the particles, or nonlinear drag. With many particles obeying these microscopic rules of motion we find that their spatial confinement to a square with periodic boundaries (which is an indirect form of attraction) leads to stable macroscopic ordering. As a function of the strength of added noise we see—at finite system sizes—a critical slowing down close to the order-disorder boundary and a discontinuous transition. After varying the density of particles at constant system size and varying the size of the system with constant particle density we predict that in the infinite system size (or density) limit the hysteresis loop disappears and the transition becomes continuous. We note that animals, humans, drones, etc., tend to move asynchronously and are often more responsive to motion than positions. Thus, for them velocity-based continuous models can provide higher precision than coordinate-based models. An additional characteristic and realistic feature of the model is that convergence to the ordered state is fastest at a finite density, which is in contrast to models applying (discontinuous) explicit velocity alignments and discretized time. To summarize, we find that the investigated model can provide a minimal description of flocking.

关键词： ELECTROSTATIC flocking ANISOTROPY INTELLIGENT agents (computer software) UBIQUITOUS computing DISCRETIZATION methods computer engineering

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Research on a dynamic integration method of isomerous multi-source data for the hot rolling process

Research on a dynamic integration method of isomerous multi-...

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2014 International Conference on Artificial Intelligence and Industrial Application (AIIA2014)

作者： D.Z.Zhang L.M.Zhao W.Adeli X.Y.Wang H.Li School of Computer and Communication Engineering University of Science and Technology Beijing Beijing Key Laboratory of Knowledge Engineering for Materials Science Department of Information Management China Petroleum Corporation Urumqi Petrochemical Corporation School of Automation and Electrical Engineering University of Science and Technology

ISBN: (纸本)9781784660529

In this paper, we describe the current situation of data integration and the feature of the traditional data integration method, we analyse the data characteristics of the hot rolling process of modern steel enterprises, and present a new dynamic data integration method, including the dynamic integration of isomerous multi-source data, the dynamic creation of a database along with a metadata management mechanism. Combined with features of data in the hot rolling process, this method could resolve the problem of the dynamic integration of isomerous multi-source data in the hot rolling process, and offer an opportunity for further comprehensive in-depth data analysis.

关键词： hot rolling process isomerous dynamic integration metadata management

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Identifying influential nodes in complex networks for network immunization

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Journal of Computational information Systems 2014年第20期10卷 8767-8774页

作者： Zhong, Lu Gao, Chao Zhang, Zili Shi, Ning Huang, Jiajin College of Computer and Information Science Southwest University Chongqing China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun China School of Information Technology Deakin University VIC Australia International WIC Institute Beijing University of Technology Beijing China

Identifying influential nodes is of theoretical significance in network immunization which is one of important methods to prevent virus propagation through protecting the influential nodes in a network. Lots of methods have been proposed to find these influential nodes based on the topological characteristics of a network (e.g., degree, betweenness or K-shell). Whereas due to the diversity of network topologies, these methods are not always effective in identifying influential nodes in any benchmark networks. We combine the advantages of existing methods based on attribute ranking and propose a universal ranking method, namely MAF (Multiple Attribute Fusion), to identify influential nodes from a complex network. We compare the efficiency of our proposed method with existing immunization strategies in different types of networks. Simulation results in the interactive email model show that the immunized nodes selected by MAF can restrain virus propagation effectively.

关键词： Complex networks

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