Hybrid genetic algorithms and support vector machines for bankruptcy prediction

By Sung-Hwan Min, Jumin Lee, Ingoo Han. Expert systems with applications, 2006

395 citations recorded by the source corpus.

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Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: a survey (2010, methodology): “Genetic algorithms (Abdelwahed and Amir 2005; Ignizio and Soltys 1996; Wallrafen et al. 1996; Min et al. 2006; Ahn et al. 2006; Yeung et al. 2007) and rough sets (Zhou and Tian 2007; Ahn et al.”
A hybrid genetic algorithm for the multi-depot vehicle routing problem (2008, background): “2005), the bankruptcy prediction (Min et al., 2006), the flow shop scheduling problem (Wang et al.”
Applied Predictive Modeling (2013, neutral): “2002), image analysis (Bhanu and Lin 2003), and finance (Min et al. 2006; Huang et al. 2012).”
Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches (2014, methodology): “[81] also proposed a GA-SVM hybrid model for bankruptcy prediction, but they used GA to optimize both feature selection and SVM parameters, to significantly improve the predictive ability of SVM.”
Kernel Methods in Finance (2008, methodology): “(Min et al., 2006) observed that the hybridised model significantly outperformed the common benchmarks logistic”
Oppositional biogeography-based optimization (2009, methodology): “In finance, EC has been employed for bankruptcy [84, 85, 86] and stock predictions [87, 88, 89].”