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🏛️國立陽明交通大學機構典藏

Author: search.filters.author.Yang, Wei-PangHas files: No

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Now showing 1 - 6 of 6
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    Access Control in XML Repositories Using a Role-Based Approach
    (2010-12-01)
    Chen, Shihn-Yuarn
    ;
    Ke, Hao-Ren
    ;
    Tsai, Wen-Nung
    ;
    Yang, Wei-Pang
    ;
    資訊工程學系
    Nowadays, more and more Internet and Web applications store data in XML documents, which are more structured than unstructured plain text files. Administrators of an XML repository want to control the accessibility of both an XML file and an element within an XML file. However, traditional access control methods do not consult the semi-structure characteristics of XML documents. In view of this, this work proposes Integrated Role-based and XML document Access Control (IRXAC), which encompasses the Role Based Access Control phase and the XML Document Access Control phase, as a solution of access control on an XML repository. The former phase performs file-level access control and decides the accessibility of an XML file. The latter phase performs element-level access control and decides the accessibility of an element. Besides the details of IRXAC, the comparisons of IRXAC with other access control methods are shown.
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    Item type:Item,
    A top-down and greedy method for discretization of continuous attributes
    (2007)
    Lee, Chien-, I
    ;
    Tsai, Cheng-Jung
    ;
    Yang, Ya-Ru
    ;
    Yang, Wei-Pang
    ;
    資訊工程學系
    Experiments show that CAIM discretization algorithm is superior to all the other top-down discretization algorithms. However CAIM algorithm does not take the data distribution into account. The discretization formula used in CAIM also gives a high factor to the numbers of generated intervals. The two disadvantages make CAIM may generate irrational discrete results in some cases and further leads to the decrease of predictive accuracy of a classifier In this paper we propose the Class-Attribute Contingency Coefficient discretization algorithm. The experimental results showed that compared with CAIM, our method can generate a better discretization scheme to bring on the improvement of accuracy of classification. With regard to the number of generated rules and execution time of a classifier CACC and CAIM achieve comparable results.
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    A decision tree-based approach to mining the rules of concept drift
    (2007)
    Lee, Chien-, I
    ;
    Tsai, Cheng-Jung
    ;
    Wu, Jhe-Hao
    ;
    Yang, Wei-Pang
    ;
    資訊工程學系
    In a database, the concept of an example might change along with time, which is known as concept drift. When the concept drift occurs, the classification model built by using old dataset is not suitable for predicting new coming dataset. Although many algorithms had been proposed to solve this problem, they focus only on updating the classification model. However in a real life users might be very interested in the rules of concept drift. For example, doctors would desire to know the main causes more for disease variation since such rules would enable them to diagnose patients more correctly and quickly. In this paper we propose a Concept Drift Rule mining Tree to accurately discover the rule of concept drift. The main contributions of this paper are: a) we address the problem of mining concept-drifting rule which was ignored in the past; b) our method can accurately mine the rule of concept drift.
  • Thumbnail Image
    Item type:Item,
    Access Control in XML Repositories Using a Role-Based Approach
    (2010-12-01)
    Chen, Shihn-Yuarn
    ;
    Ke, Hao-Ren
    ;
    Tsai, Wen-Nung
    ;
    Yang, Wei-Pang
    ;
    資訊工程學系
    Nowadays, more and more Internet and Web applications store data in XML documents, which are more structured than unstructured plain text files. Administrators of an XML repository want to control the accessibility of both an XML file and an element within an XML file. However, traditional access control methods do not consult the semi-structure characteristics of XML documents. In view of this, this work proposes Integrated Role-based and XML document Access Control (IRXAC), which encompasses the Role Based Access Control phase and the XML Document Access Control phase, as a solution of access control on an XML repository. The former phase performs file-level access control and decides the accessibility of an XML file. The latter phase performs element-level access control and decides the accessibility of an element. Besides the details of IRXAC, the comparisons of IRXAC with other access control methods are shown.
  • Thumbnail Image
    Item type:Item,
    Combining textual and visual features for cross-language medical image retrieval
    (2006)
    Cheng, Pei-Cheng
    ;
    Chien, Been-Chian
    ;
    Ke, Hao-Ren
    ;
    Yang, Wei-Pang
    ;
    資訊工程學系
    In this paper we describe the technologies and experimental results for the medical retrieval task and automatic annotation task. We combine textual and content-based approaches to retrieve relevant medical images. The content-based approach containing four image features and the text-based approach using word expansion are developed to accomplish these tasks. Experimental results show that combining both the content-based and text-based approaches is better than using only one approach. In the automatic annotation task we use Support Vector Machines (SVM) to learn image feature characteristics for assisting the task of image classification. Based on the SVM model, we analyze which image feature is more promising in medical image retrieval. The results show that the spatial relationship between pixels is an important feature in medical image data because medical image data always has similar anatomic regions. Therefore, image features emphasizing spatial relationship have better results than others.
  • Thumbnail Image
    Item type:Item,
    An efficient and sensitive decision tree approach to mining concept-drifting data streams
    (2008)
    Tsai, Cheng-Jurig
    ;
    Lee, Chien-I
    ;
    Yang, Wei-Pang
    ;
    資訊工程學系
    ;
    Department of Computer Science
    Data stream mining has become a novel research topic of growing interest in knowledge discovery. Most proposed algorithms for data stream mining assume that each data block is basically a random sample from a stationary distribution, but many databases available violate this assumption. That is, the class of an instance may change over time, known as concept drift. In this paper, we propose a Sensitive Concept Drift Probing Decision Tree algorithm (SCRIPT), which is based on the statistical X(2) test, to handle the concept drift problem on data streams. Compared with the proposed methods, the advantages of SCRIPT include: a) it can avoid unnecessary system cost for stable data streams b) it can immediately and efficiently corrects original classifier while data streams are instable; c) it is more suitable to the applications in which a sensitive detection of concept drift is required.