A New Approach to an On-line Learning System and Its Application in Tax Case Selection

• Main Authors: Kuo-Lung Hsieh, 謝國龍
• Other Authors: Mu-Chun Su
• Format: Others
• Language: zh-TW
• Published: 2003
• Online Access: http://ndltd.ncl.edu.tw/handle/51680950328156126436

碩士 === 國立中央大學 === 資訊工程學系碩士在職專班 === 91 === For an efficient pattern recognition system, it is important to possess the property of the on-line learning. The on-line learning property is referred to the ability of learning new classes and refining existing classes quickly and without destroying old class information. Recently, many on-line learning systems have been proposed. One of the most famous on-line learning systems is the Fuzzy Adaptive Resonance Theory Map (Fuzzy ARTMAP). Despite many appealing properties, the Fuzzy ARTMAP system suffers from the computation overhead and architectural redundancy. In this paper, a new on-line learning system based on the simplified Fuzzy ARTMAP is proposed. The proposed system can be applied in pattern recognition and function approximation problems. The performance of the proposed on-line learning system is evaluated by not only some artificial data sets but also the tax data set. The only way employed by Taiwan tax office to investigate business income tax evasion is to reexamine some randomly selected case. Owing to limited human resources, the performance of the investigation is usually not effective and efficient. In this thesis, the proposed on-line learning system was trained to improve the performance of investigating tax evasion. Targets of this research are (1) to achieve an acceptable degree of accurate prediction, (2) to prevent inspectors from overloading, and (3) to stop the speculative phenomenon of tax evasion.