Mining financial distress trend data using penalty guided support vector machines based on hybrid of particle swarm optimization and artificial bee colony algorithm

Neurocomputing

Volumn 82, Issue , 2012, Pages 196-206

  Hsieh, Tsung Jung ^a   Hsiao, Hsiao Fen ^b   Yeh, Wei Chang ^a,c  

^a NATIONAL TSING HUA UNIVERSITY   (Taiwan)

^b MINGDAO UNIVERSITY   (Taiwan)

^c UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

Author keywords

Corporate governance; Earnings management; Evolutionary artificial bee colony algorithm; Financial failure; Penalty guided support vector machines

Indexed keywords

ARTIFICIAL BEE COLONIES; CORPORATE GOVERNANCE; EARNINGS MANAGEMENT; FINANCIAL FAILURE; SUPPORT VECTOR;

BACKPROPAGATION ALGORITHMS; ECONOMIC AND SOCIAL EFFECTS; FINANCE; INDUSTRY; MATHEMATICAL MODELS; NEURAL NETWORKS; SUPPORT VECTOR MACHINES; VECTORS;

PARTICLE SWARM OPTIMIZATION (PSO);

ALGORITHM; ANALYTIC METHOD; ARTICLE; ARTIFICIAL BEE ALGORITHM; ARTIFICIAL NEURAL NETWORK; BACK PROPAGATION; DATA ANALYSIS; DATA BASE; DATA MINING; FINANCIAL MANAGEMENT; INDUSTRY; PARTICLE SWARM OPTIMIZATION; PREDICTION; PRIORITY JOURNAL; PROCESS OPTIMIZATION; RELIABILITY; SAMPLING; STATISTICAL MODEL; SUPPORT VECTOR MACHINE; TAIWAN; TREND STUDY; VALIDATION PROCESS; VELOCITY;

EID: 84856500674     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2011.11.020     Document Type: Article

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