| Summary: | 碩士 === 元智大學 === 資訊管理學系 === 99 === ABSTRACT
The business tax is a consumption tax charged at the point of purchase for certain goods and services. Nowadays, the business tax has become one of the important taxes for regional taxes, because the tax dodging and the tax evasion are very serious, and the amount of amercement about the business tax dodging and the business tax evasion is the highest among all the tax amercement. Getting to the bottom of the issue, the reason is that the fictitious company is the prime criminal of corrupting the business tax.
In this paper, we used an efficient data mining machine, support vector machine (SVM), which proves to perform well in prediction problems, in fictitious company detection. A fictitious company presentiment model is established to prevent the the tax dodging and the tax evasion. All the materials (including the management and the company) of business taxes cases from 1990 to 2010 is used to test our established support vector machine based prediction system. The pre-processing result of 1,576 samples is served as the training data of SVM, and the others are used to investigate the performance of SVM.
The simulation and the analysis on the database indicate that SVM is more feasible than the artificial neural network, and it has rapider convergence and more stable training result than the artificial neural network, so achieving high accuracy in prediction. By an analysis on the sensitivity on the variables, we found that the whether the principal has the substantive due tax, occupy the other (and the exceptional) companies or not are more important than the others, which can be used as a reference in fictitious company detection.
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