Decision Degree-based Decision Tree Technology for Rule Extraction

Abstract

[1] A. Skowron, J. Stepaniuk, and R. Swiniarski, “Modeling Rough Granular Computing Based on Approximation Spaces”, Information Sciences, vol. 184, no.1, pp.20–43, 2012.
http://dx.doi.org/10.1016/j.ins.2011.08.001

[2] L. Sun, J. C. Xu, Z. A. Xue, and L. J. Zhang, “Rough Entropy-Based Feature Selection and Its Application”, Journal of Information and Computational Science, vol.8, no.9, pp.1525–1532, 2011.

[3] L. Sun, J. C. Xu, and S. Q. Li, “Knowledge Reduction Based on Granular Computing from Decision Information Systems”, Lecture Notes in Computer Science, vol.6401, pp.46–53, 2010.
http://dx.doi.org/10.1007/978-3-642-16248-0_12

[4] L. Sun, J. C. Xu, S. Q. Li, X. Z. Cao, and Y. P. Gao, “New Approach for Feature Selection by Using Information Entropy”. Journal of Information and Computational Science, vol.8, no.12, pp.2259–2268, 2011.

[5] L. Sun, J. C. Xu, S. Q. Li, and Y. P. Gao, “Rough Entropy Extensions for Feature Selection under Incomplete Decision Information Systems”, Advances in Information Sciences and Service Sciences, vol.3, no.11, pp.264–274, 2011.
http://dx.doi.org/10.4156/aiss.vol3.issue11.33

[6] J. C. Xu and L. Sun, “A New Knowledge Reduction Algorithm Based on Decision Power in Rough Set”, Transactions on Rough Sets, vol.XII, pp.76–89, 2010.
http://dx.doi.org/10.1007/978-3-642-14467-7_4

[7] G. Y. Wang, “Rough Reduction in Algebra View and Information View”, International Journal of Intelligent Systems, vol.18, no.6, pp.679–688, 2003.
http://dx.doi.org/10.1002/int.10109

[8] L. P. An and L. Y. Tong, “Binary Relations as A Basis for Rule Induction in Presence of Quantitative Attributes”, Journal of Computers, vol.5, no.3, pp. 440–447, 2010.

[9] L. Sun, J. C. Xu, and L. J. Zhang, “Approaches to Knowledge Reduction of Decision Systems Based on Conditional Rough Entropy”, International Journal of Advancements in Computing Technology, vol.3, no.9, pp.129–139, 2011.
http://dx.doi.org/10.4156/ijact.vol3.issue9.17

[10] R. Q. Chang, Z. Pei, and C. Zhang, “A Modified Editing K-Nearest Neighbor Rule”, Journal of Computers, vol.6, no.7, pp.1493–1500, 2011.
http://dx.doi.org/10.4304/jcp.6.7.1493-1500

[11] Y. H. Qian, J. Y. Liang, and C. Y. Dang. “Converse Approximation and Rule Extraction from Decision Tables in Rough Set Theory”, Computers and Math-ematics with Applications, vol.55, no.8, pp. 1754–1765, 2008.
http://dx.doi.org/10.1016/j.camwa.2007.08.031

[12] W. Ziarko, “Acquisition of Hierarchy-Structured Probabilistic Decision Tables and Rules from Data”, Expert Systems, vol.20, no.5, pp.305–310, 2003.
http://dx.doi.org/10.1111/1468-0394.00255

[13] J. S. Mi, Y. Leung, and W. Z. Wu, “Dependence-Space-Based Attribute Reduction in Consistent Decision Tables”, Soft Computing, vol.15, no.2, pp. 261−268, 2011.
http://dx.doi.org/10.1007/s00500-010-0656-1

[14] D. Y. Li, B. Zhang, and Y. Leung, “On Knowledge Reduction in Inconsistent Decision Information Systems”, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol.12, no.5, pp.651–672, 2004.
http://dx.doi.org/10.1142/S0218488504003132

[15] S. Greco, Z. Pawlak, and R. Slowinski, “Can Bayesian Confirmation Measures be Useful for Rough Set Decision Rules?”, Engineering Applications of Artificial Intelligence, vol.17, no.4, pp.345–361, 2004.
http://dx.doi.org/10.1016/j.engappai.2004.04.008

[16] U. Rückert, L. D. Raedt, “An Experimental Evalu-ation of Simplicity in Rule Learning”, Artificial Intelligence, vol.172, no.1, pp.19–28, 2008.
http://dx.doi.org/10.1016/j.artint.2007.06.004

[17] G. H. Chen, Y. Tang, Y. Pan, and Q. Deng, “Question Classification Using Multiple Kernel Learning and Semantic Information”, Journal of Computers, vol.6, no.11, pp.2325–2334, 2011.
http://dx.doi.org/10.4304/jcp.6.11.2325-2334

[18] S. N. Pang and N. Kasabov, “Encoding and Decoding the Knowledge of Association Rules over SVM Classification Trees”, Knowledge and Information Systems, vol.19, no.1, pp.79–105, 2009.
http://dx.doi.org/10.1007/s10115-008-0147-1

[19] S. Badr and A. Bargiela, “Case Study of Inaccuracies in The Granulation of Decision Trees”, Soft Computing, vol.15, no.6, pp.1129–1136, 2011.
http://dx.doi.org/10.1007/s00500-010-0587-x

[20] J. H. Zhai, “Fuzzy Decision Tree Based on Fuzzy-Rough Technique”, Soft Computing, vol.15, no.6, pp.1087–1096, 2011.
http://dx.doi.org/10.1007/s00500-010-0584-0

[21] L. X. Jiang and C. Q. Li, “Scaling up the Accuracy of Decision Tree Classifiers: A Naïve Bayes Combination”, Journal of Computers, vol.6, no.7, pp. 1325–1331, 2011.

[22] J. M. Wei, S. Q. Wang, M. Y. Wang, J. P. You, and D. Y. Liu, “Rough Set Based Approach for Inducing Decision Trees”, Knowledge-Based Systems, vol.20, no.8, pp.695–702, 2007.
http://dx.doi.org/10.1016/j.knosys.2006.10.001

[23] J. R. Quinlan, “C4.5: Programs for Machine Learning”, Morgan Kaufmann Publishers Inc. San Francisco, CA, USA, 1993.

[24] Y. S. Huang and B. C. Huang, “Recognition of Multi–Interval Rules in Dataset with Continuous–Valued Attributes”, Expert Systems with Applications, vol.36, no.2, pp.1485–1492, 2009.
http://dx.doi.org/10.1016/j.eswa.2007.11.042

[25] J. M. Wei, “Rough Set Based Approach to Selection of Node”, International Journal of Computational Cognition, vol.1, no.2, pp.25–40, 2003.

[26] Y. Liu, R. Xiong, and J. Chu, “Quick Attribute Reduction Algorithm with Hash”, Chinese Journal of Computers, vol.32, no.8, pp.1493–1499, 2009.

[27] G. Y. Wang, “Rough Set Theory and Knowledge Acquisition”, Xi'an Jiaotong University Press, China, 2001.

[28] UCI Repository of Machine Learning Databases, 1998. Available: http://ftp.ics.uci.edu/pub/machine-learning-data bases