Soft computing system for bank performance prediction
By Vadlamani Ravi, H. Kurniawan, Peter Nwee Kok Thai, P. Ravi Kumar. 2008
182 citations recorded by the source corpus.
Subfields: Accounting, Artificial Intelligence, Management Science and Operations Research
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Accounting
- Antanas Verikas
- Zivile Kalsyte
- Marija Bacauskiene
- Adas Gelzinis
- Arash Bahrammirzaee
- Jacinta Chan Phooi M’ng
- M. Mehralizadeh
- Anthony Grace
In Artificial Intelligence
- Antanas Verikas
- Zivile Kalsyte
- Marija Bacauskiene
- Adas Gelzinis
- Arash Bahrammirzaee
- Alain Rakotomamonjy
- Jacinta Chan Phooi M’ng
- M. Mehralizadeh
In Management Science and Operations Research
- Antanas Verikas
- Zivile Kalsyte
- Marija Bacauskiene
- Adas Gelzinis
- Arash Bahrammirzaee
- Jacinta Chan Phooi M’ng
- M. Mehralizadeh
- Anthony Grace
Citations in the corpus
- Deep Learning for Financial Applications : A Survey (2020, neutral): “[85], Fethi et al.”
- Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches (2014, unknown): “Ravi et al. [94] combined a multi-layered feed forward BPNN, a probabilistic NN, a radial basis function NN, SVM, DT, and a fuzzy rule classifier to be an ensemble system for FDP, and demonstrated that the ensemble yield lower Type I and II errors.”
- A survey of multiple classifier systems as hybrid systems (2014, unknown): “initial dimensionality reduction is also tested [201].”
- Assessing bank efficiency and performance with operational research and artificial intelligence techniques: A survey (2010, unknown): “1 The applications discussed in Ravi et al. (2008) were published until 2005, and”
- Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: a survey (2010, methodology): “Ravi et al. (2008) aggregated nine classifiers of different architecture, to build an ensemble for bankruptcy prediction.”
- A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems (2010, methodology): “[256] Bank performance prediction Ensemble MLFF-BPNN, PNN, RBF, SVM, CART and a fuzzy rule-based classifier and using GRNN and GA for training Its constituent models and MDA and human judgment Hybrid model performs better”
- Financial credit risk assessment: a recent review (2015, unknown): “Table 2 Some hybrid structural multiple classifier systems Work Composite classifiers Chen and Ribeiro (2013) LR, SVM, C45, ADTree, KNN, MLP, Decision Table, RBF, Bayesian, Bayesian LR Hung and Chen (2009) DT, MLP, SVM Ravi et al. (2008) MLP, RBFN, PNN, SVM, CART, fuzzy rule-based classifier, PCA-ML”