Abstract
Classical rough set theory has shown powerful capability in attribute dependence analysis, knowledge reduction and decision rule extraction. However, in some applications where the subjective and apriori knowledge must be considered, such as cost-sensitive learning and class imbalance learning, classical rough set can not obtain the satisfying results due to the absence of a mechanism of considering the subjective knowledge. This paper discusses problems connected with introducing the subjective knowledge into rough set learning and proposes a weighted rough set learning approach. In this method, weights are employed to represent the subjective knowledge and a weighted information system is defined firstly. Secondly, attribute dependence analysis under the subjective knowledge is performed and weighted approximate quality is given. Finally, weighted attribute reduction algorithm and weighted rule extraction algorithm are designed. In order to validate the proposed approach, experimentations of class imbalance learning and cost-sensitive learning are constructed. The results show that the introduction of appropriate weights can evidently improve the performance of rough set learning, especially, increasing the accuracy of the minority class and the AUC for class imbalance learning and decreasing the classification cost for cost-sensitive learning.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Fawcett, R.E., Provost, F.: Adaptive fraud detection. Data Mining and Knowledge Discovery 3(1), 291–316 (1997)
Japkowicz, N., Stephen, S.: The Class Imbalance Problem: A Systematic Study. Intelligent Data Analisis 6(5), 429–450 (2002)
Weiss, G.M., Provost, F.: The Effect of Class Distribution on Classifier Learning: an Empirical Study. Technical Report ML-TR-44, Rutgers University, Department of Computer Science (2001)
Japkowicz, N.: Learning from Imbalanced Data Sets: A Comparison of Various Strategies. In: Working Notes of the AAAI’00 Workshop Learning from Imbalanced Data Sets, pp. 10–15 (2000)
Weiss, G., Provost, F.: Learning When Training Data are Costly: The Effect of Class Distribution on Tree Iinduction. Journal of Artificial Intelligence Research 19, 315–354 (2003)
Maloof, M.A.: Learning When Data Sets are Imbalanced and When Costs Are Unequal and Unknown. In: Proc. Working Notes ICML’03 Workshop Learning from Imbalanced Data Sets (2003)
Ting, K.M.: An Instance-Weighting Method to Induce Cost-Sensitive Trees. IEEE Trans. Knowledge and Data Eng. 14(3), 659–665 (2002)
Brefeld, U., Geibel, P., Wysotzki, F.: Support Vector Machines with Example Dependent Costs. In: Lavrač, N., et al. (eds.) ECML 2003. LNCS (LNAI), vol. 2837, pp. 23–34. Springer, Heidelberg (2003)
Zhou, Z.-H., Liu, X.-Y.: Training Cost-Sensitive Neural Networks with Methods Addressing the Class Imbalance Problem. IEEE Trans. Knowledge and Data Eng. 18(1), 63–77 (2006)
Pawlak, Z.: Rough Sets. International Journal of Computer and Information Sciences 11, 341–356 (1982)
Xu, C.-Z., Min, F.: Weighted Reduction for Decision Tables. In: Wang, L., et al. (eds.) FSKD 2006. LNCS (LNAI), vol. 4223, pp. 246–255. Springer, Heidelberg (2006)
Ma, T.-H., Tang, M.-L.: Weighted Rough Set Model. In: Sixth International Conference on Intelligent Systems Design and Applications, pp. 481–485 (2006)
Hu, Q.-H., et al.: Fuzzy Probabilistic Approximation Spaces and Their Information Measures. IEEE Transactions on Fuzzy Systems 14(2), 191–201 (2006)
Grzymala-Busse, J.W.: LERS - a System for Learning from Examples Based on Rough Sets. In: Slowinski, R. (ed.) Intelligent Decision Support, pp. 3–18. Kluwer Academic Publishers, Dordrecht (1992)
Blake, C., Keogh, E., Merz, C.J.: UCI Repository of Machine Learning Databases, Dept. of Information and Computer Science, Univ. of California, Irvine (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
Fayyad, U., Irani, K.: Discretizing Continuous Attributes While Learning Bayesian Networks. In: Proc. Thirteenth International Conference on Machine Learning, pp. 157–165. Morgan Kaufmann, San Francisco (1996)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Liu, J., Hu, Q., Yu, D. (2007). Weighted Rough Set Learning: Towards a Subjective Approach. In: Zhou, ZH., Li, H., Yang, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2007. Lecture Notes in Computer Science(), vol 4426. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71701-0_75
Download citation
DOI: https://doi.org/10.1007/978-3-540-71701-0_75
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71700-3
Online ISBN: 978-3-540-71701-0
eBook Packages: Computer ScienceComputer Science (R0)