Revenue Evaluation Based on Rough Set Reasoning
Budget revenue evaluation is an important issue in planing economic policies, especially in making profit by financial investment. It is dealt with this paper, a method of rating turnover efficiency using the methodology of rough set-a technique for data mining, and information entropy- a measurement of the average amount of information contained in an information system. The proposed method, firstly reduces superfluous data sources, and then extracts important decision rules from data set. By using this method, organizations of economic management and corporations management can evaluate of economic policies, business strategies and determine what revenue sources are efficient and need be invested. Through testing of collected dataset, it is shown that the proposed method is feasible and need be developed in practice.
KeywordsRough set information entropy mutual information decision rules ACO algorithm financial evaluation
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- 1.Thang, V.N.: Developing Prediction Model on National Budget Revenue in Vietnam. UNDP Report, in Project: Strengthen Decision and Supervision Capacity on Budget of Elective Institutions in Vietnam, Committe of Finance and Budget - Vietnam National Parliament. Hanoi, Vietnam (2012)Google Scholar
- 3.Ahna, B.S., Chob, S.S., Kim, C.Y.: The Integrated Methodology of Rough Set Theory and Artificial Neural Network for Business Failure Prediction. Expert Systems with Applications, Pergamon 18, P II, 65–74 (2000)Google Scholar
- 6.Chen, Y., Wang, S., Chan, C.-C.: Application of Rough Sets to Patient Satisfaction Analysis. In: Proc. of the 11th Intl’ DSI and the 16th APDSI Joint Meeting, Taipei, Taiwan (2011)Google Scholar
- 7.Hua, Q., Zhangb, L., Chenc, D., Pedryczd, W., Yua, D.: Gaussian kernel based fuzzy rough sets: Model. Uncertainty measures and Applications. Intl’ Jour. of Approximate Reasoning (2010), doi:10.1016/j.ijar.2010.01.004Google Scholar
- 8.Pawlak, Z., Skowron, A.: Rough sets: Some extensions. Information Sciences (177), 28–40 (2007)Google Scholar
- 9.Jensen, R.: Combining rough and fuzzy sets for feature selection. Dr. Thesis, pp. 112-120. School of Informatics, University of Edinburgh (2005)Google Scholar
- 10.Gray, R.M.: Entropy and Information Theory, Stanford University, pp. 17–47. Springer, New York (2009)Google Scholar
- 11.Nguyen, K.P., Tu, H.T.: Data mining based on Rough set theory. A chapter in the book: Knowledge Discovery in Databases. Academy Publisher Inc., Quastisky Building, P.O. Box 4389, USA (2013)Google Scholar
- 12.Dorigo, M., Birattari, M., Stutzle, T.: Ant Colony Optimization. IEEE Computational Intelligence Magazine, 1556-603X/06/$20.00©2006IEEE, 28–39 (2006)Google Scholar