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Learning hybrid Bayesian network based on divide and conquer strategy

Journal of computational Information Systems 2007年第2期3卷 583-588页

作者： Wang, Limin Xu, Peijuan Li, Xiongfei Li, Xiaolin Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Changchun 130012 China Department of Computer Science and Technology Nanjing University Nanjing 210093 China

We augment Naive Bayes models by using divide and conquer strategy to address shortcomings of the standard Naive Bayes text classifier. The result is a generalized Bayes classifier which allows for local dependence among feature subset;a model we refer to as the Augmented Naive Bayes (ANB) classifier. ANB relaxes the independence assumptions of Naive Bayes while still permitting efficient inference and learning. Experimental studies on a set of natural domains show that ANB has clear advantages with respect to the generalization ability.

关键词： Bayesian networks

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Communities Detection with Applications to Real-World Networks Analysis

Communities Detection with Applications to Real-World Networ...

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International Conference on computational Intelligence and Security

作者： Bo Yang School of Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University Changchun China

Community structure is one of non-trivial topological properties ubiquitously demonstrated in real-world complex networks. Related theories and approaches are of fundamental importance for understanding the functions of networks. Previously, we have proposed a probabilistic algorithm called the NCMA to efficiently as well as effectively mine communities from real-world networks. Here, we show that the NCMA can be readily extended and applied to address a wide range of network oriented applications beyond community detection including ranking, characterizing and searching real world networks.

关键词： Communities Probability distribution Dolphins Social network services Probabilistic logic Prediction algorithms Clustering algorithms

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Friend Recommendation System Based on Geolocation and Contents of Users

Friend Recommendation System Based on Geolocation and Conten...

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2018 International Conference on Computer Modeling, Simulation and Algorithm（CMSA2018）

作者： Mingyue Duan Jing Huang Jiaxuan Zhang Yunjie Tian Dongwei Li Bin Wang College of Computer Science and Technology Jilin university Key Laboratory of Symbol Computation and Knowledge Engineering (Jilin university) Ministry of Education

ISBN: (纸本)9781510864696

In recent years, with the rapid development of wireless mobile network and smart phone operating systems, various social software based on wireless Internet has emerged one after another. Current popular social software such as QQ and We Chat have become important tools for people to meet new friends. At present, existing social software can recommend other users in the vicinity according to the geographical location of the user. However, this method does not consider the user's interests, hobbies, etc. So that the effectiveness of such a friend recommmendation system is often unsatisfactory. In order to solve the above problems, a personalized friend recommendation system based on geolocation information and user content is designed and developed. In this system, not only the geolocation information of the user is considered, but also the features of the user's published statuses are extracted, aiming to recommend more similar other users to the user. After testing, the effectiveness of the proposed method is verified.

关键词： instant message friend recommendation geolocation information context information

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Learning OWL ontologies from free texts

Learning OWL ontologies from free texts

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： He Hu Da-You Liu Open Symbol Computation and Knowledge Engineering Laboratory of State Education Committee College of Computer Science and Technology Jilin University Changchun China School of Information Renmin University of China Beijing China

ISBN: (纸本)0780384032

Ontology based approach has been popularized by current semantic Web researches. However, ontology building by hand has proven to be a very hard and error-prone task and become the bottleneck of ontology acquiring process. WordNet, an electronic lexical database, is considered to be the most important resource available to researchers in computational linguistics. The paper proposes an ontology learning approach, which uses WordNet lexicon resources to build a standard OWL ontology model. The approach can the automation of ontology building and be very useful in ontology-based applications.

关键词： OWL Ontologies Semantic Web Buildings Automation Humans knowledge engineering Laboratories Computer science education Educational institutions

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The BBC News Hunter: A Novel Crawler for BBC News

The BBC News Hunter: A Novel Crawler for BBC News

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The Second International Conference of Young Computer Scientists, Engineers and Educators,ICYCSEE 2016

作者： Mingxin Wang Ning Wang Boran Wang Can Tian Yanchun Liang Guozhong Zhao Xiaosong Han College of Software Jilin University Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Jilin University Zhuhai Laboratory of Key Laboratory for Symbol Computation and Knowledge Engineering of Ministry of Education Zhuhai College of Jilin University Daqing Oilfield Personnel Development Institute CNPC

In order to distinguish and extract the topic information from other interferential information on the BBC news website for the study in social computing, the BBC News Hunter was proposed in this paper. The whole system consists of 6 subsystems, respectively named: UI, Control, Download, Analysis,Storage and Log. Numerical experiments show that satisfactory results can be obtained from the BBC news website, whose average accuracy as well as efficiency are acceptable.

关键词： BBC Crawler News HTML parser Multithread

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A study on improved hidden Markov models and applications to speech recognition

A study on improved hidden Markov models and applications to...

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International Conference on Computer Science and Service System (CSSS)

作者： Zeliang Zhang Xiongfei Li Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China Beihang University Jilin China

Visual voice lip-reading, so the computer can understand what the speakers want to express direction by looking at their lips. Lip reading is the easiest way to compare the early characters and templates from the frozen image is stored. It ignores the very nature and time changes. This method is very simple, but it's just simple elements can be classified, then it may not show significant speech recognition services. Behavior was characterized by more and more common. Because of the hidden Markov model is superior (HMM), which can be widely used in speech recognition. In recent years, is also used to lip reading identification. Classical HMM model, so that the two assumptions: hidden assumptions collected: in t+1 the state can only be in this country is not in the state before t; from the hidden visible state hypothesis: only by regulating the t hide the visible state, rather than the previous state. This hypothesis is not very useful in some applications (such as lip reading) is reasonable. Under certain conditions, in the t state not only limits the t-1, but also t-2. Therefore, this study modified the assumptions of the classical HMM to derive a new HMM model and algorithms, and applied to the lip-reading recognition is increasing discrimination.

关键词： Hidden Markov models Mouth Speech recognition Cost accounting Training Viterbi algorithm Algorithm design and analysis

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An Ant Colony Optimization Method for Prize-collecting Traveling Salesman Problem with Time Windows

An Ant Colony Optimization Method for Prize-collecting Trave...

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International Conference on Natural computation (ICNC)

作者： Xiaohu Shi Liupu Wang You Zhou Yanchun Liang Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University Changchun China

Focused on a variation of the Euclidean traveling salesman problem (TSP), namely the prize-collecting traveling salesman problem with time windows (PCTSPTW), this paper presents a novel ant colony optimization solving method. The time window constraints are considered in the computation for the probability of selection of the next city. The parameters of the algorithm are analyzed by experiments. Numerical results also show that the proposed method is effective for the PCTSPTW problem.

关键词： Ant colony optimization Traveling salesman problems Cities and towns Algorithm design and analysis Time factors Performance analysis Educational institutions Computer science Educational technology Laboratories

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Robust estimation of the fundamental matrix based on an error model

Robust estimation of the fundamental matrix based on an erro...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Hui-Xiang Zhong Yue-Ping Feng Yun-Jie Pang College of Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University Changchun China

A new method is presented for robustly estimating fundamental matrix from matched points. The method comprises two parts. The first uses a robust technique - the random sample consensus (RANSAC) to discard outliers in an initial set of matched points. It adopts the sampling strategy to generate inliers from the initial set. The second part of the method is an algorithm for computing fundamental matrix, using the output of the RANSAC. This algorithm is based on the consistent fundamental matrix estimation in a quadratic measurement error model. An extended system for determining the estimator is proposed, and an efficient implementation for solving the system - a continuation method is developed. The proposed algorithm avoids solving total eigenvalue problems. Results for both synthetic and real images show the effectiveness of the proposed method.

关键词： Robustness Measurement errors Computer errors Eigenvalues and eigenfunctions Computer vision Layout Educational institutions Computer science Educational technology Laboratories

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Explanation Chains Model Based on the Fine-Grained Data 8th

Explanation Chains Model Based on the Fine-Grained Data

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8th CCF International Conference on Natural Language Processing and Chinese Computing, NLPCC 2019

作者： Ma, Fu-Yuan Chen, Wen-Qi Xiao, Min-Hao Wang, Xin Wang, Ying College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Changchun130012 China Changchun Institute of Technology Changchun130012 China College of Software Jilin University Changchun130012 China

ISBN: (纸本)9783030322359

With the development of information society, Recommendation System has been an import tool to help users filter information and create more economic value for enterprises. However, it is difficult for traditional recommendation systems to interpret recommendation results. In order to improve users’ trust in recommendation results, interpretable recommendation models have attracted more and more attention. In this paper, we present the Explanation Chains Model based on the Fine-grained Data (F-ECM) to enhance the effectiveness of recommendation while achieving the interpretability of recommendation. First, we generate parsing trees from user comments and extract three key sentence structure information (i.e., aspects, features and sentiment tendency) from those generated parsing tree. The fine-grained similarity is computed based on the aspects and features of the products to be recommended, and users’ product satisfaction is predicted by combining sentiment tendency. Then the recommendation chain will be constructed according to the satisfaction degree in the recommendation list. Finally, we calculate recommendation chain scores of all the items to be recommended to the target user, generate the recommendation results and personal explanation for the user by the recommendation chains. Experiments in the Amazon data set show that the Explanation Chains Model based on the Fine-grained Data achieve better interpretability and performance of product recommendation systems. © 2019, Springer Nature Switzerland AG.

关键词： Recommender systems

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PNESR-DDI: An Effective Drug-Drug Interaction Prediction Model Based on Pretraining Method and Enhanced Subgraph Reconstruction

PNESR-DDI: An Effective Drug-Drug Interaction Prediction Mod...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Ke Chen Xiaosong Han Xiaoran Li Yanchun Liang Dong Xu Renchu Guan Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Software Jilin University Changchun China Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Jilin University Changchun China Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education School of Computer Science Zhuhai College of Science and Technology Zhuhai China Department of Electrical Engineering and Computer Science Christopher S. Bond Life Sciences Center University of Missouri Columbia USA

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Drug-Drug Interaction (DDI) task plays a crucial role in clinical treatment and drug development. Recently, deep learning methods have been successfully applied for DDI prediction. However, training deep learning models always need large amount of data, while known DDIs are scarce. To address this challenge, a graph neural network-based DDI prediction model named PNESR-DDI is proposed, which compensates for the lack of DDIs by enriching drug representations. First, to obtain initial node representations that incorporate rich semantic information from the biomedical knowledge graph (KG), a link prediction pre-training method on external KG is proposed in the node embedding pre-training module. Then, considering the large scale of the KG, subgraph extraction for the target drug pairs is introduced to reduce noise and decrease computational complexity in the subgraph anchoring module. After that, the subgraph is updated, and node similarities are propagated in the subgraph reconstruction module. Based on the node similarity scores, the subgraph is pruned and reconstructed, which adjusts node representations to be more conducive to DDI prediction. Finally, the drug embeddings, subgraph representations, and drug fingerprint features are concatenated to predict DDIs. PNESRDDI is evaluated on two benchmark DDI datasets: DrugBank and TWOSIDES. Experiment results show that PNESR-DDI achieves better performance than baselines. Ablation results validate the effectiveness of the pre-training method and the adaptive subgraph reconstruction strategy.

关键词： Drugs Deep learning Adaptation models Visualization Biological system modeling Noise knowledge graphs Predictive models Fingerprint recognition Transformers

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