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Using Hybrid Classifiers to Conduct Intangible Assets Evaluation

Using Hybrid Classifiers to Conduct Intangible Assets Evaluation

Yu-Hsin Lu, Yu-Cheng Lin
Copyright: © 2016 |Volume: 7 |Issue: 1 |Pages: 19
ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781466691209|DOI: 10.4018/IJAMC.2016010102
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MLA

Lu, Yu-Hsin, and Yu-Cheng Lin. "Using Hybrid Classifiers to Conduct Intangible Assets Evaluation." IJAMC vol.7, no.1 2016: pp.19-37. http://doi.org/10.4018/IJAMC.2016010102

APA

Lu, Y. & Lin, Y. (2016). Using Hybrid Classifiers to Conduct Intangible Assets Evaluation. International Journal of Applied Metaheuristic Computing (IJAMC), 7(1), 19-37. http://doi.org/10.4018/IJAMC.2016010102

Chicago

Lu, Yu-Hsin, and Yu-Cheng Lin. "Using Hybrid Classifiers to Conduct Intangible Assets Evaluation," International Journal of Applied Metaheuristic Computing (IJAMC) 7, no.1: 19-37. http://doi.org/10.4018/IJAMC.2016010102

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Abstract

Traditional financial reporting usually ignores intangible assets, even though these assets play an increasingly important role in today's knowledge-based economy. As such, the valuation of intangible assets, while typically overlooked in traditional reporting, has nonetheless garnered widespread interest. This paper uses data-mining technologies to identify important valuation factors and to determine an optimal valuation model. In the feature selection process, the paper focus on three methods, namely, decision trees, association rules, and genetic algorithms in data mining, to identify important valuation factors. The results show that decision trees have approximately 75% prediction accuracy and select seven critical variables. In the prediction process, the paper constructs and compares many kinds of evaluation and prediction models. The results show that hybrid classifiers (i.e., k-means + k-NN) perform best in terms of prediction accuracy (91.52%), Type I and II errors (11.17% and 7.15%, respectively), and area under ROC curve (0.908).

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