A New Rough Set Based Classification Rule Generation Algorithm(RGA)

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• 作者：Honghai Feng (23) (24)
  Yanyan Chen (25)
  Kaiwei Zou (23)
  Lijuan Liu (23)
  Qiannan Zhu (23)
  Zhuo Ran (23)
  Li Yao (23)
  Lijin Ji (23)
  Sai Liu (23)
  
• 关键词：Rough sets ; classification rule ; C4.5 ; JRIPPER ; CBA
• 刊名：Lecture Notes in Computer Science
• 出版年：2014
• 出版时间：2014
• 年：2014
• 卷：8481
• 期：1
• 页码：369-378
• 参考文献：1. Quinlan, R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo (1993)
  2. Cohen, W.W.: Fast Effective Rule Induction. In: Twelfth International Conference on Machine Learning, pp. 115鈥?23 (1995)
  3. Pawlak, Z.: Rough sets. International Journal of Computer and Information Sciences聽11, 341鈥?56 (1982) CrossRef
  4. Liu, B., Hsu, W., Ma, Y.: Integrating Classification and Association Rule Mining. In: Fourth International Conference on Knowledge Discovery and Data Mining, pp. 80鈥?6 (1998)
  5. http://www.lcb.uu.se/tools/rosetta/
  6. Thabtah, F.A., Cowling, P.I.: A greedy classification algorithm based on association rule. Applied Soft Computing聽7, 1102鈥?111 (2007) CrossRef
  7. Yin, X., Han, J.: CPAR: classification based on predictive association rule. In: Proceedings of the SDM, San Francisco, CA, pp. 369鈥?76 (2003)
  8. Lim, T.-S., Loh, W.-Y.: A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-Three Old and New Classification Algorithms. Machine Learning聽40, 203鈥?28 (2000) CrossRef
  9. Thabtah, F., Cowling, P., Hammoud, S.: Improving rule sorting, predictive accuracy and training time in associative classification. Expert Systems with Applications聽31, 414鈥?26 (2006) CrossRef
  10. Li, R., Wang, Z.-O.: Mining classification rules using rough sets and neural networks. European Journal of Operational Research聽157, 439鈥?48 (2004) CrossRef
  11. Murphy, P.M., Aha, D.W.: UCI repository of machine learning databases, machine-readable data repository, Irvine, CA, University of California, Department of Information and Computer Science (1992)
  12. Fayyad, U.M., Irani, K.B.: Multi-interval discretization of continuousvalued attributes for classification learning. In: Thirteenth International Joint Conference on Articial Intelligence, pp. 1022鈥?027 (1993)
  
• 作者单位：Honghai Feng (23) (24)
  Yanyan Chen (25)
  Kaiwei Zou (23)
  Lijuan Liu (23)
  Qiannan Zhu (23)
  Zhuo Ran (23)
  Li Yao (23)
  Lijin Ji (23)
  Sai Liu (23)
  
  23. School of Computer and Information Engineering, Henan University, Kaifeng, Henan, 475001, China
  24. Institute of Data and Knowledge Engineering, Henan University, Kaifeng, Henan, 475001, China
  25. Library, Henan University, Kaifeng, Henan, 475001, China
  
• ISSN：1611-3349

文摘

Rough sets theory has taken an important role in data mining. This paper introduces a new rough set based classification rule generation algorithm. It has three features: the first is that the new algorithm can be used in inconsistent systems. The second is its ability to calculate the core value without attributes reduction before. The third is that every example gives a rule and the core values are added first in rule generation process. Experimental results indicate that the classification performanceismuch better than the standard rough set, its variants andJRIPPER, a little better thanCBA and KNN,andcompetive to C4.5in terms of 8 measures. The higher performance of the new algorithm may get benefit from its enough higher accuracy rules and having some properties like KNN.