Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks

By Esteban Alfaro Cortés, Noelia García Rubio, Matías Gámez, David A. Elizondo. Decision Support Systems, 2008

298 citations recorded by the source corpus.

Subfields: Accounting, Artificial Intelligence, Finance

View Paper: https://www.semanticscholar.org/paper/11a8b00ebaaa4cf436bd2634763cb1ad17e6e096

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Citations in the corpus

Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: a survey (2010, methodology): “DT An ensemble of DT created using the AdaBoost algorithm (Alfaro et al. 2008; Cortes et al. 2007).”
A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation (2023, background): “…of applications in several domains such as intrusion detection Hu et al. [108], distributed and scalable online learning [109], bankruptcy forecasting [110], in health sciences [111], demand-driven acquisition (DDA) prediction [112], in sports [113], etc. Adaboost has been prominent in CI problems.”
Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction (2016, background): “Recently, it has been shown that the ensemble classifier can be successfully applied to the bankruptcy prediction (Nanni and Lumini, 2009) and it significantly beats other methods (Alfaro et al., 2008).”
Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches (2014, methodology): “Bagging and Boosting (including Ada- Boost) ensembles based on NN were widely applied in FDP researches such West et al. [126], Alfaro et al. [2], and Kim and Kang [56].”
Sales forecasting using extreme learning machine with applications in fashion retailing (2008, background): “This measure considerably accelerates weight learning and avoids saturation or overflow of the hidden and output neurons whose activation values generally fall in the [0,1] or [1,1] interval.”
Forecasting energy consumption using ensemble ARIMA-ANFIS hybrid algorithm (2016, result): “Alfaro, García [48] compared the results of AdaBoost and neural network techniques in the field of forecasting by about thirty percent decrease in generalization error, and deduced the priority of AdaBoost method results.”
Comparative analysis of data mining methods for bankruptcy prediction (2012, background): “Among the thousands of refereed journal articles, many recent studies have applied neural networks (NNs) [1,3,18,19,23,24,27,33,34,43,44,46,48].”