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Machine Learning Approach for Taxation Analysis using Classification Techniques
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 12 - Number 10 |
| Year of Publication: 2011 |
| Authors: R.Deepa Lakshmi, N.Radha |
10.5120/1723-2322
|
R.Deepa Lakshmi, N.Radha . Machine Learning Approach for Taxation Analysis using Classification Techniques. International Journal of Computer Applications. 12, 10 ( January 2011), 1-6. DOI=10.5120/1723-2322
Abstract
Data mining process discovers useful information from the hidden data, which can be used for future prediction. Machine learning provides methods, techniques and tools, which help to learn automatically and to make accurate predictions based on past observations. The data are retrieved from the real time environmental setup. Machine learning techniques can help in the integration of computer-based systems in predicting the dataset and to improve the efficiency of the system. The main purpose of this paper is to provide a comparison of some commonly employed classification algorithms under same conditions. Such comparison helps to provide the accurate result in algorithms. Hence comparing the algorithms for such a classifier is a tedious task, for real time dataset. The classification models were experimented by using 365 datasets with 24 attributes. The predicted values for the classifiers were evaluated and the results were compared.
References
- Ian H. Witten, Eibe Frank, Len Trigg, Mark Hall, Geoffrey Holmes, Sally Jo Cunningham, “WEKA: Practical Machine Learning Tools and Techniques with Java Implementations,”.
- Tulai, A., Oppacher, F., 2004. Maintaining Diversity and Increasing the Accuracy the Accuracy of Classification Rules through Automatic Speciation. Congress of Evolutionary Computation, Portland, USA, 2241-2248
- Introduction to Machine Learning and Data Mining: Peng Du, Wenxiang Yao.
- Alm, J., (1999). “Tax Compliance and Administration,In Handbook on Taxation; eds. Hildreth, W. B.,Richardson, J. A.,pp. 741-768. Marcel Dekker, Inc.
- Weka 3: Data Mining Software in Java http:// www.cs .waikato .ac.nz/ml/weka/.
- Jiawei Han and Micheline Kamber (2001). Data Mining: concepts and techniques. Academic Press,San Diego, California.
- Cecil, Wayne H. (1998) Assuring Individual Taxpayer Compliance: Audit rates, Selection Methods, and Electronic Auditing. The CPA Journal, 68, (12), available at http://www.nysscpa.org/cpajournal/ 1998/ 1198/ Departments/D661198.html, last accessed 27 Sep 2007
- Data Mining: Practical Machine Learning Tools and Techniques with JAVA Implementation, by I. H. Witten and E. Frank, Morgan Kanfmann Publishers, 2000.
- Murray, Mathew N. (1995) Sales Tax Compliance and Audit Selection. National Tax Journal. 48, (4), 515-30.
- Michalski RS, Kaufman K. Learning patterns in noisy data: the AQ approach. In: Paliouras G, Karkaletsis V,Spyropoulos C, editors. Machine learning and its applications. Berlin: Springer-Verlag; 2001. p. 22–38.
- M. Pazzani and D. Kibler, The Utility of Knowledge in Inductive Learning, Machine Learning, Vol. 9, No. 1, 1992, pp. 57-94.
- Micci-Barreca Daniele, Ramachandran Satheesh.(2006) Analytics Elite. Predictive Tax Compliance Management.
- Witten IH, Frank E. Data Mining: Practical Machine Learning Tools and Techniques. Second edition, 2005. Morgan aufmann
- GfAlbrecht, C.C., Albrecht, W.S. and Dunn, J.G. (2001), “Can auditors detect fraud: a review of the research evidence”, Journal of Forensic Accounting, Vol. 2 No. 1, pp. 1-12.
- Kalousis, A., Theoharis, T., “ NDesign, Iimplementation and performance results of an intelligent assistant for classifier selection”, In: Intelligent Data Analysis, (1999).
- U. M. Fayyad, G. Piatetsky-Shapiro, P.Smyth, and R. G. R. Uthurusamy, “Advances in Knowledge Discovery and Data Mining”, AAAI Press / The MIT Press, Menlo Park, CA. 1996.
- T. Mitchell, "Machine learning", Ed. Mc Graw-Hill International Editions, 1997.
- Teknomo, Kardi. K-Nearest Neighbors Tutorial.
- Chen S., “Nonlinear time series modeling and prediction using Gaussian RBF networks with enhanced clustering and RLS learning”, Inst. Elect. Eng. Electron. Lett.31:17– 118, 1995.
- Tulai.A., Oppacher, F., “Multiple Species Weighted Voting – a Genetic-Based Machine Learning System”, Genetic and Evolutionary Computation Conference, Seattle, USA, 1263-1274.
- Watts, R. L., and J. L. Zimmerman, 1986, Positive Accounting Theory.Prentice-Hall.
- Inza I., Larranaga P. and Sierra B.,” Feature Subset Selection by Bayesian Networks: A Comparison with Genetic and Sequential Algorithms”, International Journal of Approximate Reasoning 27, pp143-164, 2001.
- Brazdil, P.B., Soares, C., Da Costa, J.P.: Ranking Learning Algorithms: Using IBL and Meta-Learning on Accuracy and Time Results. Machine Learning 50 (2003) 251-277.
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