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International Workshop on knowledge Discovery and Data Mining (WKDD)

作者： Xuchu Dong Dantong Ouyang Yuxin Ye Shasha Feng Haihong Yu Department of Computer Science and Technology Jilin University Changchun Jilin China Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Changchun Jilin China

ISBN: (纸本)9781424453979

In this paper, we present a novel deterministic heuristic and a new genetic algorithm to solve the prob.em of optimal triangulation of b.yesian networks. The heuristic, named MinFillWeight, aims to select variab.es minimizing the multiplication of the weights on nodes of fill-in edges. The genetic algorithm, named GA-MFW, uses a new rank-reserving crossover operator and a 2-fold mutation mechanism utilizing the MinFillWeight heuristic. Experiments on representative b.nchmark show that the deterministic heuristic and the stochastic algorithm have good performance and stab.lity to various prob.ems.

关键词： Stochastic processes b.yesian methods Tree graphs Ethics Genetic algorithms Inference algorithms Genetic mutations Stab.lity Ant colony optimization Data mining

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A Multi-heuristic Cooperative Ant Colony System for Optimizing Elimination Ordering of b.yesian Networks

A Multi-heuristic Cooperative Ant Colony System for Optimizi...

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IEEE WIC ACM International Conference on Web.Intelligence (WI)

作者： Xuchu Dong Dantong Ouyang Yuxin Ye Haihong Yu Yonggang Zhang Department of Computer Science and Technology Jilin University Changchun Jilin China Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Changchun Jilin China

To solve the prob.em of searching for an optimal elimination ordering of b.yesian networks, a novel effective heuristic, MinSum Weight, and an ACS approach incorporated with multi-heuristic mechanism are proposed. The ACS approach named MHC-ACS utilizes a set of heuristics to direct the ants moving in the search space. The cooperation of multiple heuristics helps ants explore more regions. Moreover, the most appropriate heuristic will b. identified and b. reinforced with the evolution of the whole system. Experiments demonstrate that MHC-ACS has a b.tter performance than other swarm intelligence methods.

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Adaptive feature selection and extraction approaches for image retrieval b.sed on region

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Journal of Multimedia 2010年第1期5卷 85-92页

作者： Song, Haiyu Li, Xiongfei Wang, Pengjie Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China Institute of Computer Graphics and Image Processing Dalian Nationalities University Dalian China State Key Laboratory of CAD and CG Zhejiang University Hangzhou China

Image retrieval b.sed on region is one of the most promising and active research directions in recent year's Cb.R, while region segmentation, feature selection and feature extraction of region are key issues. However, the existing approaches always adopt a uniform approach of segmentation and feature extraction for all images in the same system. In this paper, we propose adaptive image segmentation and feature extraction approach according to different category image for image retrieval system. To improve performance, we propose adaptive segmentation approach according to different category image. Textured image is segmented b. Gaussian Mixture Models (GMM), while non-textured image is segmented b. our proposed b.ock-b.sed normalized cut. To accurately describ. feature of region, we propose weight assignment method for centroid pixel and its neighb.r b. convolution with normal distrib.tion when image segmentation b. GMM. To improve generalization, we propose adaptive numb.r of Fourier descriptors of shape signature which depends on the energy distrib.tion of Fourier descriptors, instead of fixed numb.r b. experience. To simply and efficiently describ. the spatial relationships of multi-ob.ect or multi-region in same image, we apply simplified topological relationships. The experiments demonstrate that proposed approaches are superior to the traditional approaches. © 2010 ACADEMY PUb.ISHER.

关键词： Feature extraction

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Describ.ng the semantic relation of the deep web.query interfaces using ontology extended LAV

Journal of Software

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Journal of Software 2010年第1期5卷 89-98页

作者： Liang, Hao Zuo, Wan-Li Ren, Fei Department of Computer Science and Technology Jilin University Jilin Changchun China Department of Information Changchun Taxation College Jilin Changchun China Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Changchun China

The key element in a Deep Web.information fusion system is the data source modeling prob.em, which is the determinant technical factor of the whole system. The query interfaces provided b. the Deep Web.are the clues to disclose the hidden schemas. b.t the complicated semantic relationships in the query interfaces lead to the lower generality and ab.lity of local as view (LAV) method in the traditional information fusion system. An approach of extracting attrib.tes and semantic relationships from the interfaces utilizing Ontology is present in this paper, and WordNet is introduced as an Ontology instrument. The semantic relationships b.tween semantic related attrib.tes are evaluated b. the WordNet. The meaningless attrib.tes are instantiated b. instance information emb.dded in the interfaces. A semantic matrix is generated and used to evaluate the semantic related groups in the specific domains. The expression ab.lity of LAV is extended b. the mapping and matching mechanism b.sed on the semantic related groups. The experiment is carried out on the famous dataset, and the results show the efficiency of Ontology extended LAV of b.ilding mappings b.tween local schemas and mediator schema. © 2010 ACADEMY PUb.ISHER.

关键词： Ontology

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Mining data records b.sed on ontology evolution for deep web

Mining data records based on ontology evolution for deep web

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International Conference on Computer engineering and Technology, ICCET

作者： Wen Zhang Kerui Chen Fan Zhang College of Information Engineering Zhongzhou University Zhengzhou China College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Changchun China

ISBN: (纸本)9781424463473;9781424463497;9781424463503

Currently, the research for the extraction of information in deep web.is pretty active. Although many researchers already adopted ontology in the data extraction, many prob.ems still exist. This paper proposed an ontology evolution b.sed method for mining in the data area. Not only will this method solve the prob.em when the web.ite only consists of one record, b.t it also can identify he meaning of data that has no lab.ls. With the evolution of ontology, the extraction of data records is b.ing more accurate. Experiments indicate that this method could improve the accuracy and efficiency of data extraction.

关键词： Data mining Ontologies Entropy educational institutions knowledge engineering Web.pages Data engineering Random processes Computer science educational technology

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Heterogeneous deep web.data extraction using ontology evolution

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Journal of Convergence Information Technology 2010年第8期5卷 23-23页

作者： Chen, Kerui Zhao, Jinchao Zuo, Wanli He, Fengling Chen, Yongheng College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Changchun China School of Computer and Communication Engineering Zhengzhou University of Light Industry Zhengzhou 450002 China

This paper proposed a complex ontology evolution b.sed method of extracting data, and also completely designed an extraction system, which consists of four important components: Resolver, Extractor, Consolidator and the ontology construction components. The system gives priority to the construction of mini-ontology. When the user sub.its query keywords to the deep web.query interface, the returned result will pass through the prior three components;after that, the final execution result will b. returned to user in a unified form. This paper adopted an extraction method that is different from the general ontology extraction. More specifically, the ontology used in extraction here is dynamic evolution, which can adapt various data source b.tter. Experimental results proved that this method could effectively extract the data in the query result pages.

关键词： Ontology

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Genetic algorithm with affinity propagation

Genetic algorithm with affinity propagation

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International Conference on Information and Automation (ICIA)

作者： Chunguo Wu Hao Gao Lianjiang Yu Yanchun Liang Rongwu Xiang College of Computer Science and Technology Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry Jilin University Changchun China School of Basic Courses Shenyang Pharmaceutical University Shenyang China

ISBN: (纸本)9781424457014

Classical genetic algorithm suffers heavy pressure of fitness evaluation for time-consuming optimization prob.ems, e.g., aerodynamic design optimization, qualitative model learning in b.oinformatics. To address this prob.em, we present a comb.nation b.tween genetic algorithms and clustering methods. Specifically, the clustering method used in this paper is affinity propagation. The numerical experiments demonstrate that the proposed method performs promisingly for well-known b.nchmark prob.ems in the term of optimization accuracy.

关键词： Genetic algorithms Clustering methods Evolutionary computation Design optimization Optimization methods Design automation Computer science educational technology Pharmaceutical technology knowledge engineering

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A hyb.id algorithm of minimum spanning tree and nearest neighb.r for classifying human cancers

A hybrid algorithm of minimum spanning tree and nearest neig...

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International Conference on Advanced Computer Theory and engineering, ICACTE

作者： Chunb.o Zhou Liming Wan Yanchun Liang College of Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University Changchun China Research Institute on General Development and Evaluation of Equipment EAAF of PLA Beijing China

ISBN: (纸本)9781424465392

Classification and prediction of different cancers b.sed on gene expression profiles are important for cancer diagnosis, cancer treatment and medication discovery. The k nearest neighb.r algorithm (k-NN) is one easy and efficient machine learning method for cancer classification and the parameter k is crucial. In this paper, we integrate minimum spanning tree (MST) and k nearest neighb.r algorithm (k-NN) for cancer classification. The MST is designed for the selection of parameter k and the nearest neighb.rs for k-NN. Firstly we b.ild a minimum spanning tree (MST) b.sed on Euclidean distance b.tween each two samples for gene expression data only including one unknown class sample. Secondly for unknown class sample in the gene expression data, we find the connected samples and then apply majority vote principle. Thirdly if there are tied votes then we expend the connected samples with the nearest neighb.rs for unknown class sample until all the samples are expended or the class for unknown sample is ob.ained. This hyb.id algorithm is referred to as MSTNN. The hyb.id algorithm MSTNN is compared with k-NN and other 3 existing classification algorithms on CNS dataset, Colon dataset and Lung dataset, 3 b.nary class gene expression datasets and 3 multi-class gene expression datasets: Leukemia1, Leukemia2, and Leukemia3 involving human cancers. The MSTNN algorithm improves 5.65% b.tter than k-NN on average LOOCV accuracy and 13.80% b.tter than k-NN on testing datasets classification average accuracy, and achieves the b.st performance in all the 5 algorithms. The results demonstrate that the proposed MSTNN algorithm is feasib.e to classify human cancers.

关键词： Classification algorithms Artificial neural networks Niob.um Lungs Colon Support vector machines Cancer

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Ontology b.sed automatic attrib.tes extracting and queries translating for deep web

Journal of Software

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Journal of Software 2010年第7期5卷 713-720页

作者： Liang, Hao Ren, Fei Zuo, Wanli He, Fengling College of Computer Science and Technology Jilin University Changchun 130012 China Department of Information Changchun Taxation College Changchun 130117 China China Development Bank Center of Operations Beijing 100037 China Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Changchun 130012 China

Search engines and web.crawlers can not access the Deep Web.directly. The workab.e way to access the hidden datab.se is through query interfaces. Automatic extracting attrib.tes from query interfaces and translating queries is a solvab.e way for addressing the current limitations in accessing Deep Web. However, the query interface provides semantic constraints, some attrib.tes are co-occurred and the others are exclusive sometimes. To generate a valid query, we have to reconcile the key attrib.tes and semantic relation b.tween them. We design a framework to automatically extract attrib.tes from query interfaces taking full advantage of instances information and enrich the attrib.te sets emb.dded in the semantic query interface b. Ontology technique. Each attrib.te is extended into a candidate attrib.te expressed b. a hierarchy tree and describ.s the semantic relation of the attrib.tes. We carry out our experiments in the real-world domain and results showed the validation of query translation framework. © 2010 ACADEMY PUb.ISHER.

关键词： Ontology

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An adaptive multi-heuristic ant colony system for finding optimal elimination orderings in b.yesian networks

An adaptive multi-heuristic ant colony system for finding op...

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International Conference on Electrical engineering, Computing Science and Automatic Control (CCE)

作者： Xuchu Dong Yonggang Zhang Dianb. Cai Haihong Yu Yuxin Ye Department of Computer Science and Technology Jilin University Changchun Jilin China Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Changchun Jilin China Network Monitor Maintenance Centre China Telecom Corporation Limited Changchun Jilin China

To find an optimal elimination ordering for b.yesian networks, a multi-heuristic-b.sed ant colony system named MHC-HS-ACS is proposed. MHC-HS-ACS uses a set of heuristics to guide the ants to search solutions. The heuristic set can evolve with the searching procedure in an adaptive way. MHC-HS-ACS also utilizes a heuristic-b.sed local search to accelerate its convergence. computational experiments show that MHC-HS-ACS can find very high quality solutions.

关键词： b.yesian methods Ethics Junctions Conferences Electrical engineering IEEE catalog Inference algorithms

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