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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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Author: search.filters.author.Yang, Wei-Pang

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Now showing 1 - 10 of 15
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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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    A top-down and greedy method for discretization of continuous attributes
    (2007)
    Lee, Chien-, I
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    Tsai, Cheng-Jung
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    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.
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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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    Knowledge-based mobile learning framework for museums
    (2006)
    Hsu, Tien-Yu
    ;
    Ke, Hao-Ren
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    Yang, Wei-Pang
    ;
    圖書館
    ;
    Library
    Purpose - The purpose of this study is to propose a knowledge-based mobile learning framework that integrates various types of museum-wide content, and supports ubiquitous, context-aware, personalized learning for museums. Design/methodology/approach - A unified knowledge base with multi-layer reusable content structures serves as the kernel component to integrate content from exhibitions for education and collection in a museum. The How-Net approach is adopted to build a unified natural and cultural ontology. The ontology functions as a common and sharable knowledge concept that denotes each knowledge element in the unified knowledge base, and associates each learner's learning context and usage with a content and usage profile respectively. Data mining algorithms, e.g. association mining and clustering, are applied to discover useful patterns for ubiquitous personalization from these content and usage profiles. Findings - A pilot project based on the proposed framework has been successfully implemented in the Life Science Hall of the National Museum of Natural Science (NMNS), Taiwan, demonstrating the feasibility of this framework. Originality/value - This study proposes a mobile learning framework that can be replicated in many museums. This framework improves learners' learning experiences with rich related content, and with ubiquitous, proactive and adaptive services. Museums can also benefit from implementing this framework through outreach services for educational, promoting and usability needs from combining mobile and Internet communication technologies and learning services.
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    Combining textual and visual features for cross-language medical image retrieval
    (2006)
    Cheng, Pei-Cheng
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    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.
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    Designing a classifier by a layered multi-population genetic programming approach
    (2007-08-01)
    Lin, Jung-Yi
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    Ke, Hao-Ren
    ;
    Chien, Been-Chian
    ;
    Yang, Wei-Pang
    ;
    資訊工程學系
    This paper proposes a method called layered genetic programming (LAGEP) to construct a classifier based on multi-population genetic programming (MGP). LAGEP employs layer architecture to arrange multiple populations. A layer is composed of a number of populations. The results of populations are discriminant functions. These functions transform the training set to construct a new training set. The successive layer uses the new training set to obtain better discriminant functions. Moreover, because the functions generated by each layer will be composed to a long discriminant function, which is the result of LAGEP, every layer can evolve with short individuals. For each population, we propose an adaptive mutation rate tuning method to increase the mutation rate based on fitness values and remaining generations. Several experiments are conducted with different settings of LAGEP and several real-world medical problems. Experiment results show that LAGEP achieves comparable accuracy to single population GP in much less time. (C) 2007 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
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    An efficient and sensitive decision tree approach to mining concept-drifting data streams
    (2008)
    Tsai, Cheng-Jurig
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    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.
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    Classifier design with feature selection and feature extraction using layered genetic programming
    (2008-02-01)
    Lin, Jung-Yi
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    Ke, Hao-Ren
    ;
    Chien, Been-Chian
    ;
    Yang, Wei-Pang
    ;
    資訊工程學系
    This paper proposes a novel method called FLGP to construct a classifier device of capability in feature selection and feature extraction. FLGP is developed with layered genetic programming that is a kind of the multiple-population genetic programming. Populations advance to an optimal discriminant function to divide data into two classes. Two methods of feature selection are proposed. New features extracted by certain layer are used to be the training set of next layer's populations. Experiments on several well-known datasets are made to demonstrate performance of FLGP. (C) 2007 Elsevier Ltd. All rights reserved.
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    A discretization algorithm based on Class-Attribute Contingency Coefficient
    (2008-02-01)
    Tsai, Cheng-Jung
    ;
    Lee, Chien-I.
    ;
    Yang, Wei-Pang
    ;
    資訊工程學系
    ;
    Department of Computer Science
    Discretization algorithms have played an important role in data mining and knowledge discovery. They not only produce a concise summarization of continuous attributes to help the experts understand the data more easily, but also make learning more accurate and faster. In this paper, we propose a static, global, incremental, supervised and top-down discretization algorithm based on Class-Attribute Contingency Coefficient. Empirical evaluation of seven discretization algorithms on 13 real datasets and four artificial datasets showed that the proposed algorithm could generate a better discretization scheme that improved the accuracy of classification. As to the execution time of discretization, the number of generated rules, and the training time of C5.0, our approach also achieved promising results. (c) 2007 Elsevier Inc. All rights reserved.