Warianty tytułu
Języki publikacji
Abstrakty
As a special way in which the human brain is learning new knowledge, incremental learning is an important topic in AI. It is an object of many AI researchers to find an algorithm that can learn new knowledge quickly, based on original knowledge learned before, and in such way that the knowledge it acquires is efficient in real use. In this paper, we develop a rough set and rule tree based incremental knowledge acquisition algorithm. It can learn from a domain data set incrementally. Our simulation results show that our algorithm can learn more quickly than classical rough set based knowledge acquisition algorithms, and the performance of knowledge learned by our algorithm can be the same as or even better than classical rough set based knowledge acquisition algorithms. Besides, the simulation results also show that our algorithm outperforms ID4 in many aspects.
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
299--313
Opis fizyczny
Bibliogr. 21 poz., tab.
Twórcy
autor
- Institute of Computer Science and Technology, Chongqing University of Postas and Telecommunications, Chongqing, R.P. China, Bao_zhengzheng@263.net
autor
- Institute of Computer Science and Technology, Chongqing University of Postas and Telecommunications, Chongqing, R.P. China, Wanggy@cqupt.edu.cn
Bibliografia
- [1] Schlimmer. J. C., Fisher. D. A.: Case Study of Incremental Concept Induction. In Proceedings of the Fifth National Conf. on Artificial Intelligence, Los Altos, 1986
- [2] Utgoff, P. E.: Incremental induction of decision trees. Machine Learning 4 (1989) 161-186
- [3] Wang. G. Y., Shi, H. B., Deng, W.: A parallel neural network architecture based on nara model and sieving method. Chinese Journal of Computers 19(9) (1996)
- [4] Wang, G. Y., Nie, N.: PMSN: A Parallel multi-sieving neural network architecture. Journal of Computer Research and Development 36 (Suppl.) (1999) 21-25
- [5] Wang, G., Shi, H. B.: Parallel neural network architectures and their applications. In Proceedings of Int. Conf. on Neural Networks, Perth, Australia, 1995, 1234-1239
- [6] Liu, Z. Т.: An Incremental arithmetic for the smallest reduction of attributes. Chinese Journal of Electronics 27(11), (1999), 96-98
- [7] Wang, Z. H., Liu. Z. T: General and incremental algorithms of rule extraction based on concept lattice. Chinese Journal of Computers 22( 1) (1999) 66-70
- [8] Hu, X. H., Cercone, N.: Learning in relational databases: A rough set approach. Computational Intelligence 11(2) ,(1995), 323-338
- [9] Jelonek, Krawiec, K., Słowiński, R.: Rough set reduction of attributes and their domains for neural networks. Computational Intelligence 11(2) (1995) 339-347
- [10] Miao, D. Q.: The Researching of Rough Set Theory and Its Application in Machine Learning [PH.D thesis], Beijing Institute of Automation, Chinese Academy of Sciences, 1997
- [11] Fayyad, U. M., Piatetsky-Shapiro, L., Smyth, P., Uthurusamy, R.: Advances in Knowledge Discovery and Data Mining. Menlo Park. CA: AAAI Press/The MIT Press, 1996
- [12] Cercone. V., Tsuchiya, M.: Lousy Editor's Introduction. IEEE Transations on Knowledge and Data Engineering 5(6)(1993) 901-902
- [13] Piatetsky-Shapiro, L., Frawley. W. J.: Knowledge Discovery in Databases. Menlo Park, CA: AAAI Press/The MIT Press. 1991
- [14] Wang, G. Y.: Rough Set Theory and Knowledge Acquisition. Xi'an Jiaotong University Press, Xi'an, 2001
- [15] Wang, G. Y., Zheng. Z., Zhang, Y.: RIDAS-A Rough Set Based Intelligent Data Analysis System. In Proceedings of the First Int. Conf. on Machine Learning and Cybernetics, Beijing, 1991, 646-649
- [16] Su, J., Gao, J.: Metainformation based rough set incrementally rule extraction algorithm. Pattern Recognition and Artificial Intelligence 14(4) (2001) 428-433
- [17] Kou. Y. J., Wang, C. H., Huang, H. K.: An incremental algorithm for maintaining constrained association rules. Journal of Computer Research and Development 38(8) (2001) 947-951
- [18] Wang, G. Y., Liu. F.: The inconsistency in rough set based rule generation. In Ziarko, W., Yao, Y.Y. eds. Second International Conference on Rough Sets and Current Trends in Computing, LNAI 2005, Springer-Verlag, Berlin, 2001,370-377
- [19] Han. J. W., Kamber. M.: Data Mining Concepts and Techniques. Morgan Kaufmann, San Francisco, 2001.
- [20] Wojna, A.: Constraint based incremental learning of classification rules. In Ziarko, W., Yao, Y.Y. eds. Second International Conference on Rough Sets and Current Trends in Computing, LNAI 2005, Springer-Verlag, Berlin, 2001,428-435
- [21] Lu. R. Q.: Artificial Intelligence. Scientific Press, Beijing, 2002.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-article-BUS2-0005-0017