NDLI: A Novel Evaluating Model of CPV Vocational Ability Based on SVM Multilayer Classifier

Content Provider	IEEE Xplore Digital Library
Author	Shuzhen Wang Lei Yu Zhibin Liu
Copyright Year	2008
Description	Author affiliation: Econ. & Manage. Dept., North China Electr. Power Univ., Baoding (Zhibin Liu) \|\| Sch. of Bus., Agric. Univ. of Hebei, Baoding (Shuzhen Wang; Lei Yu)
Abstract	The asset valuation is specialized work, and it requests the certified public valuer (CPV) to have the very strong specialized competent ability. Along with the development of the economic in China, the reform of stock system, the tax reform, and the application of fair value in new business accounting standards, the new evaluation domain develops unceasingly, and which sets the new request to the CPV vocational ability. Aiming at the problem of how to evaluate the CPV vocational ability, this paper proposes the multi-level classification evaluating model based on improved support vector machine (SVM), which uses the SVM classification combination in series and introduces the type weight factor and sample weight factor. The model not only solves the shortcomings of small sample, high dimension, nonlinear and local minima in the traditional model, but solves the wrong classification question caused by the number imbalance of training samples and data interference. The CPV vocational ability evaluating results of 12 asset valuation companies in Hebei Province show that the model is simple and feasible, and improve the evaluating accuracy and efficiency.
Starting Page	469
Ending Page	472
File Size	316888
Page Count	4
File Format	PDF
ISBN	9780769533360
DOI	10.1109/CSSE.2008.416
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-12
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	vocational ability Project management Power generation economics CPV Nonhomogeneous media Management training Cost accounting Support vector machines comprehensive evaluation Support vector machine classification Machine learning Cities and towns Vocational training SVM multilayer classifier
Content Type	Text
Resource Type	Article

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