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    政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 >  Item 140.119/31105
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/31105

    Title: 應用遺傳規劃法於知識管理流程之知識擷取和整合機制
    GP-Based Knowledge Acquisition and Integration Mechanisms in Knowledge Management Processes
    Authors: 郭展盛
    Kuo,Chan Sheng
    Contributors: 陳春龍

    Chen,Chuen Lung
    Hong,Tzung Pei

    Kuo,Chan Sheng
    Keywords: 知識擷取
    knowledge acquisition
    knowledge integration
    genetic programming
    classification tree
    classification problem
    knowledge management
    Date: 2007
    Issue Date: 2009-09-14 09:15:44 (UTC+8)
    Abstract: 在目前的企業環境中,很多企業致力於管理和應用組織知識,來維持他們的核心能力和創造競爭優勢。有效率的管理組織知識,能減少解決問題的時間和成本,並增加組織學習和創新能力。並且,由於累積知識資源的需求,很多企業開始發展知識庫,以儲存組織及個人的知識,用來增加組織整體的效率、支援日常的運作以及企業策略的操作。

    In today’s business environment, many enterprises make efforts in managing and applying organizational knowledge to sustain their core competence and create competitive advantage. The effective management of organizational knowledge can reduce the time and cost of solving problems, improve organizational performance, and increase organizational learning as well as innovative competence. Moreover, due to the need to accumulate knowledge resources, many enterprises have devoted to developing their knowledge repositories. These repositories store organizational and individual knowledge that are used to increase overall organization efficiency, support daily operations, and implement business strategies.
    Knowledge management (KM) is the modern paradigm for effective management of organizational knowledge to improve organizational performance. The intent of KM is to emphasize knowledge flows and the main process of acquisition, integration, storage/categorization, dissemination, and application. Furthermore, extant information technologies can provide a way of enabling more effective knowledge management. One of the important issues in knowledge management is knowledge acquisition. It is an extra burden for knowledge workers to contribute their knowledge into repositories, such that designing an effective method for abating an extra burden to automatically generate organizational knowledge will play a critical role in knowledge management. A second rather important issue in knowledge management is knowledge integration from different knowledge sources. Designing a knowledge-integration method to combine multiple knowledge sources will gradually become a necessity for enterprises.
    Classification problems, such as financial prediction and disease diagnosis, are often encountered in many applications. In the past, classification trees were often generated by decision-tree methods and commonly used to solve classification problems. In this dissertation, we propose two GP-based knowledge-acquisition methods and two GP-based knowledge-integration methods to support knowledge acquisition and knowledge integration respectively in the knowledge management processes for classification tasks.
    In the two proposed knowledge-acquisition methods, the first one is fast and easy to find the desired classification tree. It may, however, generate a complicated classification tree. The second method then further modifies the first method and produces a more concise and applicatory classification tree than the first one. The classification tree obtained can be transferred into a rule set, which can then be fed into a knowledge base to support decision making and facilitate daily operations.
    Furthermore, in the two proposed knowledge-integration methods, the former method can automatically combine multiple knowledge sources into one integrated knowledge base; nevertheless, it focuses on a single time point to deal with such knowledge-integration problems. The latter method then extends the former one to handle integrating situations properly with different time points. Additionally, three new genetic operators are designed in the evolving process to remove redundancy, subsumption and contradiction, thus producing more concise and consistent classification rules than those without using them.
    Finally, the proposed methods are applied to credit card data and breast cancer data for evaluating their effectiveness. They are also compared with several well-known classification methods. The experimental results show the good performance and feasibility of the proposed approaches.
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    Description: 博士
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0893565033
    Data Type: thesis
    Appears in Collections:[資訊管理學系] 學位論文

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