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DC Field | Value | Language |
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dc.contributor.author | Lu, Y | - |
dc.contributor.author | Zeng, N | - |
dc.contributor.author | Liu, X | - |
dc.contributor.author | Yi, S | - |
dc.date.accessioned | 2016-07-19T11:39:52Z | - |
dc.date.available | 2015-01-01 | - |
dc.date.available | 2016-07-19T11:39:52Z | - |
dc.date.issued | 2015 | - |
dc.identifier.citation | Discrete Dynamics in Nature and Society, 2015:294930, (2015) | en_US |
dc.identifier.issn | 1026-0226 | - |
dc.identifier.uri | http://www.hindawi.com/journals/ddns/2015/294930/ | - |
dc.identifier.uri | http://bura.brunel.ac.uk/handle/2438/12974 | - |
dc.description.abstract | Bankruptcy prediction has been extensively investigated by data mining techniques since it is a critical issue in the accounting and finance field. In this paper, a new hybrid algorithm combining switching particle swarm optimization (SPSO) and support vector machine (SVM) is proposed to solve the bankruptcy prediction problem. In particular, a recently developed SPSO algorithm is exploited to search the optimal parameter values of radial basis function (RBF) kernel of the SVM. The new algorithm can largely improve the explanatory power and the stability of the SVM. The proposed algorithm is successfully applied in the bankruptcy prediction problem, where experiment data sets are originally from the UCI Machine Learning Repository. The simulation results show the superiority of proposed algorithm over the traditional SVM-based methods combined with genetic algorithm (GA) or the particle swarm optimization (PSO) algorithm alone. | en_US |
dc.description.sponsorship | This work was supported in part by the National Natural Science Foundation of China under Grants 61422301, 61374127, and 61104041, the Natural Science Foundation of Heilongjiang Province of China under Grant F201428, the Scientific and Technology Research Foundation of Heilongjiang Education Department under Grants 12541061 and 12541592, the 12th Five-Year-Plan in Key Science and Technology Research of agricultural bureau in Heilongjiang province of China under Grant HNK125B-04-03, the Doctoral Scientific Research Foundation of Heilongjiang Bayi Agricultural University under Grant XDB2014-12, the Foundation for Studying Abroad of Heilongjiang Bayi Agricultural University, the Natural Science Foundation for Distinguished Young Scholars of Heilongjiang Province under Grant JC2015016, Jiangsu Provincial Key Laboratory of Ebusiness, Nanjing University of Finance and Economics, under Grant JSEB201301, and the Major Program of Fujian under Grant 2012I01010428. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Hindawi Publishing Corporation | en_US |
dc.title | A new hybrid algorithm for bankruptcy prediction using switching particle swarm optimization and support vector machines | en_US |
dc.type | Article | en_US |
dc.identifier.doi | http://dx.doi.org/10.1155/2015/294930 | - |
dc.relation.isPartOf | Discrete Dynamics in Nature and Society | - |
pubs.publication-status | Published | - |
pubs.volume | 2015 | - |
Appears in Collections: | Publications |
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Fulltext.pdf | 2.21 MB | Adobe PDF | View/Open |
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