|
|
|
| Concept Lattice Based DataDriven Uncertain Knowledge Acquisition |
| WANG Yan1,2,3, WANG Guo-Yin1,2 , DENG Wei-Bing2 |
1.School of Information Science and Technology, Southwest Jiaotong University,
Chengdu 610031
2.Institute of Computer Science and Technology, Chongqing University of Posts
and Telecommunications, Chongqing 400065
3.College of Computer and Communication, Lanzhou University of Technology,
Lanzhou 730050 |
|
|
|
|
Abstract Uncertain knowledge acquisition is a problem when no prior domain knowledge is available. The relationship of knowledge uncertainties among three different knowledge presentation models, i.e. decision table, decision rule, and concept lattice, is discovered through analyzing their knowledge presentation styles. A datadriven automatic uncertain knowledge acquisition algorithm based on concept lattice is developed by using this relationship. Experimental results show that this algorithm is valid for acquiring uncertain knowledge.
|
|
Received: 01 June 2006
|
|
|
|
|
|
[1] Shi Zhongzhi. Knowledge Discover. Beijing, China: Tsinghua University Press, 2002 (in Chinese)
(史忠植.知识发现.北京:清华大学出版社, 2002)
[2] Wang Guoying. Uncertainty Measurement of Decision Table Information System. Computer Science, 2001, 28(5): 2326 (in Chinese)
(王国胤.决策信息系统中的不确定性度量.计算机科学, 2001, 28(5): 2326)
[3] Düntsch I, Gediga G. Uncertainty Measurement of Rough Set Prediction. Artificial Intelligence, 1998, 106(1): 109137
[4] Chen Xianghui, Zhu Shangjun, Ji Yindong. Uncertainty Measures of Rules Based on Entropy and Variable Precision Rough Set. Journal of Tsinghua University: Science and Technology, 2001, 47(3): 109112 (in Chinese)
(陈湘辉,朱善军,吉吟东.基于熵和变精度粗糙集的规则不确定性量度.清华大学学报:自然科学版, 2001, 47(3): 109112)
[5] Wang Guoyin, He Xiao. A SelfLearning Model under Uncertain Conditions. Journal of Software, 2003, 14(6): 10961102 (in Chinese)
(王国胤,何 晓.一种不确定性条件下的自主式知识学习模型.软件学报, 2003, 14(6): 10961102)
[6] Yin Desheng, Wang Guoyin, Wu Yu. A SelfLearning Algorithm for Decision Tree PrePruning // Proc of the 3rd International Conference on Machine Learning and Cybernetics. Shanghai, China, 2004: 21402145
[7] Ganter B, Wille R. Formal Concept Analysis. New York, USA: Springer-Verlag, 1999
[8] Fu Huaiyu, Fu Huaiguo, Njiwoua P, et al. A Comparative Study of FCABased Supervised Classification Algorithms // Proc of the 2nd International Conference on Formal Concept Analysis. Sydney, Australia, 2004: 313320
[9] Carpineto C, Romano G. Galois: An OrderTheoretic Approach to Conceptual Clustering // Proc of the 10th International Conference on Machine Learning. Amherst, USA, 1993: 3340
[10] Sahami M. Learning Classification Rules Using Lattices // Proc of the 8th European Conference on Machine Learning. Heraclion, Greece, 1995: 343346
[11] MephuNguifo E. Galois Lattice: A Framework for Concept Learning, Design, Evaluation and Refinement // Proc of the 6th International Conference on Tools with Artificial Intelligence. New Orleans, USA, 1994: 461467
[12] Xie Z P, Liu Z T. Research on Classifier Based on Lattice Structure // Proc of the Conference on Intelligent Information Processing. Beijing, China, 2000: 333338
[13] Anamika G, Naveen K, Vasudha B, et al. Incremental Classification Rules Based on Association Rules Using Formal Concept Analysis // Proc of the 4th International Conference on Machine Learning and Data Mining in Pattern Recognition. Leipzig, Germany, 2005: 1120
[14] OCI Dataset [DB/OL]. [2005-03-04]. http://www.ics.uci.edy/~mlearn/MLRepository.
[15] Hou Lijuan, Wang Guoying, Nie Neng, et al. Discretization in Rough Set Theory. Computer Science, 2000, 27(12): 8994 (in Chinese)
(侯利娟,王国胤,聂 能,等.粗糙集理论中的离散化问题.计算机科学, 2000, 27(12): 8994) |
|
|
|
2007, Vol. 20 
