The nature of statistical learning theory
The nature of statistical learning theory
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic AlgorithmsNumerical Optimizationand Constraints
Proceedings of the 6th International Conference on Genetic Algorithms
Expert Systems with Applications: An International Journal
On the performance of artificial bee colony (ABC) algorithm
Applied Soft Computing
Journal of Global Optimization
Predicting business failure using multiple case-based reasoning combined with support vector machine
Expert Systems with Applications: An International Journal
Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters
Expert Systems with Applications: An International Journal
Reducing the probability of bankruptcy through supply chain coordination
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A novel clustering approach: Artificial Bee Colony (ABC) algorithm
Applied Soft Computing
Operational causes of bankruptcy propagation in supply chain
Decision Support Systems
Bankruptcy prediction for credit risk using neural networks: A survey and new results
IEEE Transactions on Neural Networks
Computers and Electronics in Agriculture
Neurocomputing
A hybrid WA-CPSO-LSSVR model for dissolved oxygen content prediction in crab culture
Engineering Applications of Artificial Intelligence
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The 2008 financial tsunami had a serious impact on the economic development of many countries, including Taiwan. Thus, the ability to predict financial failure and their trends is crucial and attracts public and professional attention when the world enters a period of economic depression. We examined the predictive ability of the proposed support vector machines (SVM) method that uses the characteristics of a penalty function to generate predictions more efficiently. To include the properties of particle swarm optimization (PSO), an evolutionary artificial bee colony (EABC) algorithm was presented; each bee was given a velocity and flying direction to optimize the proposed penalty guided support vector machines (PGSVM). EABC-PGSVM was used to construct a reliable prediction model for public industrial firms in Taiwan. To demonstrate the advantages of EABC and the penalty function, EABC-PGSVM was compared with back-propagation neural network (BPNN), classic SVM optimized by the ABC algorithm (BSVM), and the PGSVM optimized by the ABC algorithm (BPGSVM). Two matched datasets of sample firms that were financially sound or financially distressed during 1999-2006 and 2000-2007 were selected from among the public industrial firms of Taiwan. The final model was validated using within-sample and out-of- the-sample tests. The results demonstrate that the proposed method is promising and can help corporations to prevent failure.