Loading...
|
Please use this identifier to cite or link to this item:
https://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
洪宗貝
Chen,Chuen Lung
Hong,Tzung Pei
郭展盛
Kuo,Chan Sheng |
| Keywords: | 知識擷取
知識整合
遺傳規劃
分類樹
分類問題
知識管理
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。
知識管理是現代的典範，可用來有效管理組織知識，進而改善組織績效。知識管理的目的是強調管理知識的流動及流程。在知識管理流程方面，主要區分為知識擷取、整合、儲存/歸類、散播和應用知識等程序。另外，資訊技術可用來協助知識管理，並能使知識管理更有效率。知識管理的主要議題之ㄧ是知識的擷取，由於目前知識來源的提供，主要是透過知識工作者，可是它對於知識工作者而言，是一種額外的負擔。因此，設計一個有效的方法來自動產生組織知識，以減輕他們的額外負擔，將是一個很重要的課題。第二個相當重要的議題是知識整合，由於不同來源的知識，可能造成組織知識的衝突，因此設計一個知識整合方法，把不同來源的知識整合成一個完整的知識，組織將會逐漸增加這方面的需求。
分類在很多應用中是常遭遇的問題，例如財務預測、疾病診斷等。在過去，分類規則常藉由決策樹的方法所產生，並用於解決分類的問題。在本論文中，提出兩個以遺傳規劃為基礎的知識擷取方法和兩個以遺傳規劃為基礎的知識整合方法，分別支援知識管理流程中的知識擷取和知識整合。
在兩個所提的知識擷取方法中，第一個方法是著重在快速和容易地找到想要的分類樹，但是，此方法可能會產生結構較複雜的分類樹。第二個方法是修正第一個方法，產生一個較精簡和應用性高的分類樹。這些所獲得的分類樹，能被轉換成規則集合，並匯入知識庫中，幫助企業決策的制定和日常的運作。
此外，在兩個所提的知識整合方法中，第一個方法，能自動結合多重的知識來源成為一個整合的知識，並可匯入知識庫中，但是此方法只考慮到單一時間點的整合。第二個方法則是可以解決不同時間點的知識整合問題。另外，本論文提出三個新的遺傳運算子，在演化過程中，可用來解決規則集合中有重複、包含和衝突等常見的問題，因而可以產生較精簡及一致性高的分類規則。最後，本論文採用信用卡資料及乳癌資料來驗證所提方法的可行性，實驗結果皆獲得良好的成效。
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. |
| Reference: | Alavi, M., and Leidner, D. E. (1999), “Knowledge management systems: issues, challenges, and benefits,” Communications of the Association for Information Systems, vol. 1, no. 7, pp. 1-37.
Alavi, M., and Leidner, D. E. (2001), “Review: Knowledge Management and Knowledge Management Systems: Conceptual Foundations and Research Issues,” MIS Quarterly, Vol. 25, No. 1, pp. 107 - 136.
Anand, S. S., Patrick, A. R., Hughes, J. G., & Bell, D. A. (1998), “A data mining methodology for cross-sales,” Knowledge-based Systems, Vol. 10, pp. 449–461.
Belew, R. and Booker, L. (1991), Proceedings of the Fourth International Conference on Genetic Algorithms, Morgan Kaufmann Publishers, Los Altos, CA.
Chen, G., Liu, H., Yu, L., Wei, Q., and Zhang X. (2006), “A new Approach to Classification Based on Association Rule Mining,” Decision Support Systems, Vol. 42, pp. 674– 689.
Chen, S. H. (2002), Genetic Algorithms and Genetic Programming in Computational Finance, Kluwer Academic Publishers.
Chen, S. H., and Kuo, T. W. (2002), “Evolutionary Computation in Economics and Finance: A Bibliography,” Evolutionary Computation in Economics and Finance, Physica-Verlag, Heidelberg New York, pp. 419-455.
Chien, B. C., Lin, J. Y., and Hong, T. P. (2002), “Learning Discriminant Functions with Fuzzy Attributes for Classification Using Genetic Programming,” Expert Systems with Applications, Vol. 23, pp. 31-31.
Chtioui, Y., Bertrand, D., Devaux, M., and Barba, D. (1997), “Comparison of Multilayer Perceptron and Probabilistic Neural Networks in Artificial Vision Application to the Discrimination of Seeds,” Journal of Chemometrics, Vol. 11, pp. 111 – 129.
Cox, D. R. (1970), The Analysis of Binary Data, Chapman & Hall, London.
Davenport, T. H., and Prusak, L. (1998), Working Knowledge: How Organizations Know What They Know, Harvard Business Press.
Despres, C. and Chauvel, D. (1999), “Mastering information management: Part six- knowledge management,” Financial Times, Vol. 8, pp. 4-6.
Drew, S. (1999) “Building knowledge management into strategy: making sense of a new perspective,” Long Range Planning, Vol.32, pp. 130–136.
Duda, R. O., Hart, P. E. and Stork, D. G.. (2001), Pattern Classification, Wiley Interscience.
Fernandez-Breis, J. T., and Martinez-Bejar, R. (2000), “A Cooperative tool for Facilitating Knowledge Management,” Expert Systems with Applications, Vol. 18, pp. 315-330.
Fisher, R. A. (1936), “The Use of Multiple Measurements in Taxonomic Problems,” Annals of Eugenics, Vol. 7, pp. 179–188.
Fischer, G. and Ostwald, J. (2001), “Knowledge management: problems, promises, realities, and challenges,” IEEE Intelligent Systems, Vol. 1, pp. 60-72.
Gaines, B. R. and Shaw, M. L. (1993), "Eliciting knowledge and transferring it effectively to a knowledge-based system," IEEE Transactions on Knowledge and Data Engineering, Vol. 5, No. 1, pp. 4–14.
Giarratano, J. and Riley, G.. (1993), Expert System Principles and Programming, Boston, MA: PWS.
Han, J. and Kamber, M. (2001), Data Mining: Concepts and Techniques, Morgan Kaufmann, New York.
Harun, M. H. (2002), “Integrating e-learning into the workplace,” The Internet and Higher Education, Vol. 4, pp. 301–310.
Heijst, G., Spek, R., & Kruizinga, E. (1997), “Corporate memories as a tool for knowledge management,” Expert Systems with Applications, Vol. 13, pp. 41–54.
Hicks, B. J., Culley, S. J., Allen, R. D., & Mullineux, G. (2002), “A framework for the requirements of capturing, storing and reusing information and knowledge in engineering design,” International Journal of Information Management, Vol. 22, pp. 263–280.
Huang, J. J., Tzeng, G. H. and Ong, C. S. (2006), “Two-stage genetic programming (2SGP) for the credit scoring model,” Applied Mathematics and Computation, Vol. 174, pp. 1039-1053.
Huangc, J. C. and Newell, S. (2003), “Knowledge integration processes and dynamics within the context of cross-functional projects,” International Journal of Project Management, Vol. 21, pp. 167–176.
Hwang, G. J., & Tseng, S. S. (1990), "EMCUD: A knowledge acquisition method which captures embedded meanings under uncertainty" International Journal of Man–Machine Studies, 33, 431–451.
Kelly, G. A. (1955), The Psychology of Personal Constructs, New York, Norton.
Kiang, M. Y. (2003), “A Comparative Assessment of Classification Methods,” Decision Support Systems, Vol. 35, pp. 441-454.
Koza, J. R. (1992), Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press.
Koza, J. R. (1999), Genetic Programming III: Darwinian Invention and Problem Solving, Morgan Kaufmann.
Kuo, C. S., Hong, T. P. and Chen, C. L. (2006), “Learning Classification Trees by Genetic Programming,” 2006 International Conference on Hybrid Information Technology, Cheju Island, Korea.
Kuo, C. S., Hong, T. P. and Chen, C. L. (2007), “Integrating Multiple Knowledge Sources by Genetic Programming,” The Third International Conference on Intelligent Information Hiding and Multimedia Signal Processing.
Kuo, C. S., Hong, T. P. and Chen, C. L. (2007), “A Knowledge-Acquisition Strategy based on Genetic Programming,” 2007 International Conference on Convergence Information Technology, Gyeongju, Korea, pp. 217-221.
Kwan, M. M. and Balasubramanian, P. (2003), “KnowledgeScope: Managing Knowledge in Context,” Decision Support Systems, Vol. 35, pp. 467 - 486.
Liao, S. H. (2002), “Problem solving and knowledge inertia,” Expert Systems with Applications, Vol. 22, pp. 21–31.
Liebowitz, J. (2001), “Knowledge management and its link to artificial intelligence,” Expert Systems with Applications, Vol. 20, pp.1–6.
Macintosh, A. and Kingston, F. I. (1999), “Knowledge Management Techniques: Teaching and Dissemination Concepts,” International Journal of Human-Computer Studies, Vol. 51, pp. 549-566.
McCown, R. L. (2002), “Locating agricultural decision support systems in the troubled past and socio-technical complexity of models for management,” Agricultural Systems, pp. 11–25.
Medsker, L., Tan, M. and Turban, E. (1995), "Knowledge acquisition from multiple experts: problems and issues," Expert Systems with Applications, Vol. 9, pp. 35-40.
Milton, N., Shadbolt, N., Cottam, H., and Hammersley, M. (1999), "Towards a Knowledge Technology for Knowledge Management," International Journal of Human-Computer Studies, Vol. 51, pp. 615-641.
Neely, C. J. and Weller, P. A. (1999), “Technical Trading Rules in the European Monetary System,” Journal of International Money and Finance, Vol. 18, pp. 429-458.
Neely, C. J. and Weller, P. A. (2001), “Technical Analysis and Central Bank Intervention,” Journal of International Money and Finance, Vol. 20, pp. 949-970.
Neely, C. J., Weller, P. A. and Dittmar, R. (1997), “Is Technical Analysis in Foreign Exchange Market Profitable? A Genetic Programming Approach,” Journal of Financial and Quantitative Analysis, Vol. 32. No. 4, pp. 405-426.
Nikolaev, N. and Iba, H. (2002), Genetic Programming of Polynomial Models for Financial Forecasting. Genetic Algorithms and Genetic Programming in Computational Finance, Kluwer Academic Publishers, pp. 103-123.
Nissen, M. E. (1999), “Knowledge-based Knowledge Management in the Reengineering Domain,” Decision support systems, Vol. 27, pp. 47-65.
Nissen, M. E. and Espino, J. (2000), “Knowledge Process and System Design for the Coast Guard,” Knowledge and Process Management, Vol. 7, No. 3, pp. 165-176.
Ong, C. S., Huang, J. J. and Tzeng, G. H. (2005), “Building Credit Scoring Models Using Genetic Programming,” Expert Systems with Applications, Vol. 29, pp. 41-47.
Parkins, A. D. and Nandi, A. K. (2004), “Genetic Programming Techniques for Hand Written Digit Recognition,” Signal Processing, Vol. 84, pp. 2345–2365.
Petrowski, A. and Genet, M. G. (1999), “A Classification Tree for Speciation,” Evolutionary Computation, Vol. 1, pp. 204-211.
Plessis, M. D. (2005), “Drivers of knowledge management in the corporate environment,” International Journal of Information Management, Vol. 25, pp. 193-202.
Quinlan, J. R. (1986), “Induction of Decision Trees,” Machine Learning, Vol. 1, pp. 81– 106.
Quinlan, J. R. (1993), C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers, San Mateo, CA.
Quinlan, J. R. (1997), C5.0 and see5: Illustrative examples, RuleQuest Research, http://www.rulequest.com.
Ramesh, B., & Tiwana, A. (1999), “Supporting collaborative process knowledge management in new product development teams,” Decision Support Systems, Vol. 27, pp. 213–235.
Robey, D., Boudreau, M. C., & Rose, G. M. (2000), “Information technology and organizational learning: a review and assessment of research,” Accounting Management and Information Technologies, Vol. 10, pp. 125–155.
Shaw, M. J., Subramaniam, C., Tan, G. W., & Welge, M. E. (2001), “Knowledge management and data mining for marketing,” Decision Support Systems, Vol. 31, pp. 127–137.
Shin, K. S. and Lee, Y. J. (2002), “A genetic algorithm application in backruptcy prediction modeling,” Expert Systems with Applications, Vol.6, pp. 1-9.
Staab S. and Studer, R. (2001), “Knowledge processes and ontologies,” IEEE Intelligent Systems, Vol. 1, pp. 26-34.
Stein, E. W. and Zwass, V. (1995), “Actualizing Organizational Memory with Information Systems,” Information Systems Research, Vol.6, No. 2, pp. 85-117.
Walsh, J. P., and Ungson, G. R. (1991), “Organizational Memory,” Academy of Management Review , Vol. 16, No. 1, pp. 57-91.
Wang, C. H., Hong, T. P., and Tseng, S. S. (1998a) “Integrating fuzzy knowledge by genetic algorithms,” IEEE Transactions on Evolutionary Computation, Vol. 2, No. 4, pp. 138-149.
Wang, C. H., Hong, T. P., Tseng, S. S., and Liao, C. M. (1998b), “Automatically Integrating Multiple Rule Sets in a Distributed-Knowledge Environment,” IEEE Transactions on Systems, Man, and Cybernetics, Vol. 28, No. 3, pp. 471-476.
Weiss, S. M. and Indurkya, N. (1997), Predictive Data Mining: A Practical Guide, Morgran Kaufman Publishers.
Yoo, S. B., & Kim, Y. (2002), “Web-based knowledge management for sharing product data in virtual enterprises,” International Journal of Production Economics, Vol. 75, pp. 173–183. |
| Description: | 博士
國立政治大學
資訊管理研究所
89356503
96 |
| 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
|
Files in This Item:
| File |
Size | Format | |
|---|
| index.html | 0Kb | HTML2 | 253 | View/Open |
|
All items in 政大典藏 are protected by copyright, with all rights reserved.
|
