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International Conference on Computer and information technology (CIT)

作者： Cong Cao Weimin Wang Cungen Cao Liangjun Zang Key Laboratory of Intelligent Information Processing Chinese Academy of Sciences School of Computer Science & Engineering Jiangsu University of Science and Technology Zhenjiang China Key Laboratory of Intelligent Information Processing Chinese Academy of Sciences Beijing China

To explore the association relations among disease, pathogenesis, physician, symptoms and drug, we adapt a variational Apriori algorithm for discovering association rules on a dataset of the Qing Court Medical Records. There are five types of semantic associations we intend to discover, including Disease-Pathogenesis-Drug set(DPaD), Disease-Symptoms-Drug set (DSyD), Disease-Drug set (DD), Disease-Physician-Drug set (DPhD) and Disease-Drug Category Set (DDC). To solve the synonymity problem and the data sparseness problem, we give a mapping strategy which maps pathogenesis to standardized forms and maps drugs to drug categories. With the mapping strategy the number of frequent drug sets rises from 287 to 1184. The experimental results indicate that our method with the mapping strategy is an effective way to acquire valuable semantic association rules.

关键词： Drugs Association rules Diseases Semantics Databases

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Feature extraction using composite individual genetic programming: An application to mass classification

Feature extraction using composite individual genetic progra...

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2012 International Applied Mechanics, MechatronicsAutomation and System Simulation Meeting, AMMASS 2012

作者： Lv, Yinghua Guo, Yuting Sun, Hui Zhang, Ming Wang, Jianzhong College of Humanities and Sciences of Northeast Normal University Changchun China Key Laboratory of intelligent information Processing of jinlin Universities College of Computer Science and Information Technology Northeast Normal University Changchun China National Engineering Laboratory for Druggable Gene and Protein Screening Northeast Normal University Changchun China Key Laboratory of symbolic Commputation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

This paper proposes a novel method for breast cancer diagnosis using the features generated by genetic programming (GP). We developed a new individual combination pattern (Composite individual genetic programming) which regards several individual as one unity to generate more powerful features that can improve the discriminatory performance of a classifier and reducing the input feature dimensionality at the same time. The performance of the proposed method is demonstrated by extensive experiments on MIAS and DDSM mammographic image database. © (2012) Trans Tech Publications, Switzerland.

关键词： Feature extraction

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Residual belief propagation for topic modeling

Residual belief propagation for topic modeling

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8th International Conference on Advanced Data Mining and Applications, ADMA 2012

作者： Zeng, Jia Cao, Xiao-Qin Liu, Zhi-Qiang School of Computer Science and Technology Soochow University Suzhou 215006 China Shanghai Key Laboratory of Intelligent Information Processing China School of Creative Media City University of Hong Kong Tat Chee Ave 83 Hong Kong Hong Kong

ISBN: (纸本)9783642355264

Fast convergence speed is a desired property for training topic models such as latent Dirichlet allocation (LDA), especially in online and parallel topic modeling algorithms for big data sets. In this paper, we develop a novel and easy-to-implement residual belief propagation (RBP) algorithm to accelerate the convergence speed for training LDA. The proposed RBP uses an informed scheduling scheme for asynchronous message passing, which passes fast convergent messages with a higher priority to influence those slow convergent messages at each learning iteration. Extensive empirical studies confirm that RBP significantly reduces the training time until convergence while achieves a much lower predictive perplexity than several state-of-the-art training algorithms for LDA, including variational Bayes (VB), collapsed Gibbs sampling (GS), loopy belief propagation (BP), and residual VB (RVB). © Springer-Verlag 2012.

关键词： Belief propagation

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PPLSA: Parallel probabilistic latent semantic analysis based on MapReduce 1

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7th IFIP International Conference on intelligent information processing, IIP 2012

作者： Li, Ning Zhuang, Fuzhen He, Qing Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Graduate University Chinese Academy of Sciences Beijing China Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding China

ISBN: (数字)9783642328916

ISBN: (纸本)9783642328909

PLSA(Probabilistic Latent Semantic Analysis) is a popular topic modeling technique for exploring document collections. Due to the increasing prevalence of large datasets, there is a need to improve the scalability of computation in PLSA. In this paper, we propose a parallel PLSA algorithm called PPLSA to accommodate large corpus collections in the MapReduce framework. Our solution efficiently distributes computation and is relatively simple to implement. © 2012 IFIP International Federation for information processing.

关键词： MapReduce

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Research on the method of expert homepage recognition based on Markov logic networks

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Journal of Computational information Systems 2012年第3期8卷 1089-1096页

作者： Wu, Zejian Yu, Zhengtao Su, Lei Liu, Li Xian, Yantuan School of Information Engineering and Automation Kunming University of Science and Technology Kunming 650051 China Intelligent Information Processing Key Laboratory Kunming University of Science and Technology Kunming 650051 China

For the issue that existing methods for Expert Homepage Recognition (EHP) usually identify each page separately, regardless of the relationships among the labels of candidate homepages, this paper, integrated utilizing the features of individual pages and relationships between candidate homepages, proposes a method for EHP based on Markov Logic Networks (MLNs). The method utilizes the features of words, link recall, and link type from individual pages and dependencies on all candidate pages, makes use of discriminative learning to get the weights of features, and then takes advantage of MaxWalkSAT (the weighted variant of the WalkSAT) to perform inference to achieve EHP. Experiments on the proposed method are compared with the SVM algorithm, and the experimental result shows that the method has good performance. 1553-9105/Copyright © 2012 Binary information Press.

关键词： Markov processes

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Leaf image recognition using Fourier transform based on ordered sequence

Leaf image recognition using Fourier transform based on orde...

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8th International Conference on intelligent Computing technology, ICIC 2012

作者： Yang, Li-Wei Wang, Xiao-Feng Department of Automation University of Science and Technology of China Hefei Anhui 230027 China Intelligent Computing Laboratory Hefei Institute of Intelligent Machines Chinese Academy of Sciences P.O. Box 1130 Hefei Anhui 230031 China Key Lab. of Network and Intelligent Information Processing Hefei University Hefei Anhui 230601 China

ISBN: (纸本)9783642315879

There are a number of leaf recognition methods, but most of them are based on Euclidean space. In this paper, we will introduce a new description of feature for the leaf image recognition, which represents the leaf contour with the ordered sequence. For a leaf image, points on the contour represent the most important information of the leaf. Thus, by extracting serial points of the leaf contour, the unique corresponding ordered sequence can be obtained for a contour. Then, we can compute the amplitude-frequency feature by performing the Discrete Fourier transform on the ordered sequence. Since the low-frequency part of the Fourier transform represents the global information and the high-frequency part the local details, we can adopt the amplitude-frequency feature for leaf image recognition. Experimental results on the famous Swedish library and ICL library show that the proposed feature is effective for leaf image recognition. © 2012 Springer-Verlag.

关键词： Image recognition

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A domain entity answer learning ranking method of integrating multi-feature

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Journal of Computational information Systems 2012年第3期8卷 1139-1147页

作者： Meng, Zhipeng Yu, Zhengtao Su, Lei Guo, Jianyi Zong, Huanyun School of Information Engineering and Automation Kunming University of Science and Technology Kunming 650051 China Intelligent Information Processing Key Laboratory Kunming University of Science and Technology Kunming 650051 China

In view of characteristics of the factoid question and the list question of the Question Answering System (QA), this paper proposed a domain entity answer ranking model which integrates multiple features. First, for the specific domain, we use conditional random fields to identify entities;then retrieval the candidate answers documents that related to the query, take the candidate answer documents as the bridge of the connecting the topics of the question and the answer entity, use logistic regression algorithm to build the domain entity answer ranking model with various features of domain candidate answer entity and documents and the relevance of the answers, get the relevance of the answer entity and the answer question through integrating the relevance of candidate answer documents and the topics of the query, and then rank according to the relevance. Finally, we carried on the tourism domain entity answer ranking experiment, and the results show that the precision and recall rate of the answer ranking have been improved obviously compared to other methods. 1553-9105/Copyright © 2012 Binary information Press.

关键词： Regression analysis

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Question answering oriented muti-kernel support vector data description user modeling

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Journal of Computational information Systems 2012年第16期8卷 6781-6788页

作者： Zhao, Xing Yu, Zhengtao Zou, Junjie Meng, Zhipeng Guo, Jianyi School of Information Engineering and Automation Kunming University of Science and Technology Kunming 650051 China Intelligent Information Processing Key Laboratory Kunming University of Science and Technology Kunming 650051 China

For the problem that many difierent classification of questions and answers and user's changing from one interest to another, we propose a personalized user model based on multi-kernel support for vector data domain description (MSVDD). First, user interest document is represented for user profile matrix by making use of vector space model. Then, the MSVDD method is used for descripting user profile matrix and building the optimal hypersphere that envelop all user profile. Finally, the user model is evaluated by the receiver operating characteristic (ROC) and Area Under roc Curve (AUC). The experiments show that our method is efiective. © 2012 Binary information Press.

关键词： User profile

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Combination of semantic similarity and Hidden Markov Model in Chinese question classification

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Journal of Computational information Systems 2012年第3期8卷 1131-1138页

作者： Kang, Chaoming Yu, Zhengtao Su, Lei Liu, Li Guo, Jianyi School of Information Engineering and Automation Kunming University of Science and Technology Kunming 650051 China Intelligent Information Processing Key Laboratory Kunming University of Science and Technology Kunming 650051 China

Use of probability and statistics for question classification, the classifier training only relies on the frequency of the feature words in the question, but it dose not take into account the semantic relationships between words of question. This paper presents a question classification algorithm which combines semantic similarity with sequence analysis of Hidden Markov Model. Firstly, it extracts the feature word set of all question categories as the observation sequence of different Hidden Markov Model classifiers. Secondly, use the formation and evolution process of feature words set in different types as a sequence of state transition. Finally, construct the Hidden Markov classification model for different question categories by calculating the feature word's observation probability distribution in different states. The question classification experiment in the field of tourism is conducted. The results show that the proposed method has a great improvement than the existing methods and this approach could effectively use the relationship between the words in question to classify the question. 1553-9105/Copyright © 2012 Binary information Press.

关键词： Hidden Markov models

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Parallel Web Mining System Based on Cloud Platform

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ZTE Communications 2012年第4期10卷 45-53页

作者： Shengmei Luo Qing He Lixia Liu Xiang Ao Ning Li Fuzhen Zhuang Pre-Research department of ZTE Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Graduate University of Chinese Academy of Sciences

Traditional machine-learning algorithms are struggling to handle the exceedingly large amount of data being generated by the internet. In real-world applications, there is an urgent need for machine-learning algorithms to be able to handle large-scale, high-dimensional text data. Cloud computing involves the delivery of computing and storage as a service to a heterogeneous community of recipients, Recently, it has aroused much interest in industry and academia. Most previous works on cloud platforms only focus on the parallel algorithms for structured data. In this paper, we focus on the parallel implementation of web-mining algorithms and develop a parallel web-mining system that includes parallel web crawler; parallel text extract, transform and load （ETL） and modeling; and parallel text mining and application subsystems. The complete system enables variable real-world web-mining applications for mass data.

关键词： web mining large scale high volume high dimension cloudcomputing

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