Application of neural networks for detecting erroneous tax reports from construction companies

NCUIR > Engineering College > Graduate Institute of construction engineering > journal & Dissertation > Item 987654321/50079

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/50079

Title:	Application of neural networks for detecting erroneous tax reports from construction companies
Authors:	Chen,JH;Su,MC;Chen,CY;Hsu,FH;Wu,CC
Contributors:	營建管理研究所
Keywords:	DECISION TREES;MODEL;CLASSIFICATION;PERFORMANCE;PREDICTION
Date:	2011
Issue Date:	2012-03-27 17:02:59 (UTC+8)
Publisher:	國立中央大學
Abstract:	In this study we develop an automatic detection model for discovering erroneous tax reports. The model uses a variety of neural network applications inclusive of the Multi-Layer Perceptrons (MLPs), Learning Vector Quantization (LVQ), decision tree, and Hyper-Rectangular Composite Neural Network (HRCNN) methods. Detailed taxation information from construction companies registered in the northern Taiwan region is sampled, giving a total of 5769 tax reports from 3172 construction companies which make up 35.98% of the top-three-class construction companies. The results confirm that the model yields a better recognition rate for distinguishing erroneous tax reports from the others. The automatic model is thus proven feasible for detecting erroneous tax reports. In addition, we note that the HRCNN yields a correction rate of 78% and, furthermore, generates 248 valuable rules, providing construction practitioners with criteria for preventing the submission of erroneous tax reports. (C) 2011 Elsevier B.V. All rights reserved.
Relation:	AUTOMATION IN CONSTRUCTION
Appears in Collections:	[Graduate Institute of construction engineering] journal & Dissertation

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