Determinants of intangible assets value: The data mining approach

NCUIR > School of Management at National Central University > Department of Information Management > journal & Dissertation > Item 987654321/106578

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/106578

Title:	Determinants of intangible assets value: The data mining approach
Authors:	蔡志豐;Tsai, Chih-Fong;Lu, Yu-Hsin;Yen, David C.
Contributors:	管理學院資訊管理學系
Keywords:	Companies;Data mining;Feature selection;Firm value;Intangible assets value;Neural networks;Valuation
Date:	2012-07-01
Issue Date:	2026-04-23 13:29:17 (UTC+8)
Publisher:	Elsevier;Elsevier B.V
Abstract:	摘要： It is very important for investors and creditors to understand the critical factors affecting a firm’s value before making decisions about investments and loans. Since the knowledge-based economy has evolved, the method for creating firm value has transferred from traditional physical assets to intangible knowledge. Therefore, valuation of intangible assets has become a widespread topic of interest in the future of the economy. This study takes advantage of feature selection, an important data-preprocessing step in data mining, to identify important and representative factors affecting intangible assets. Particularly, five feature selection methods are considered, which include principal component analysis (PCA), stepwise regression (STEPWISE), decision trees (DT), association rules (AR), and genetic algorithms (GA). In addition, multi-layer perceptron (MLP) neural networks are used as the prediction model in order to understand which features selected from these five methods can allow the prediction model to perform best. Based on the chosen dataset containing 61 variables, the experimental result shows that combining the results from multiple feature selection methods performs the best. GA∩STEPWISE, DT∪PCA, and the DT single feature selection method generate approximately 75% prediction accuracy, which select 26, 22, and 7 variables respectively. 出版者： Elsevier B.V 出版日期： 2012-07 出處： Knowledge-based systems, 2012-07, Vol.31, p.67-77 資源來源： Elsevier ScienceDirect Journals Complete 版權： 2012 Elsevier B.V. 識別號： ISSN: 0950-7051 識別號： EISSN: 1872-7409 識別號： DOI: 10.1016/j.knosys.2012.02.007
Appears in Collections:	[Department of Information Management] journal & Dissertation

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