Imbalance-oriented SVM methods for financial distress prediction: a comparative study among the new SB-SVM-ensemble method and traditional methods

Financial health prediction models using artificial neural networks, genetic algorithm and multivariate discriminant analysis: Iranian evidence

(2011) F. Mokhatab Rafiei et al.   EXPERT SYSTEMS WITH APPLICATIONS

Class imbalance methods for translation initiation site recognition in DNA sequences

(2011) Nicolás García-Pedrajas et al.   KNOWLEDGE-BASED SYSTEMS

A multi-objective optimisation approach for class imbalance learning

(2011) Paolo Soda   PATTERN RECOGNITION

A learning method for the class imbalance problem with medical data sets

(2010) Der-Chiang Li et al.   COMPUTERS IN BIOLOGY AND MEDICINE

Dynamic financial distress prediction using instance selection for the disposal of concept drift

(2010) Jie Sun et al.   EXPERT SYSTEMS WITH APPLICATIONS

A cross model study of corporate financial distress prediction in Taiwan: Multiple discriminant analysis, logit, probit and neural networks models

(2009) Tzong-Huei Lin   NEUROCOMPUTING

Financial distress prediction based on serial combination of multiple classifiers

(2008) Jie Sun et al.   EXPERT SYSTEMS WITH APPLICATIONS

Using neural networks and data mining techniques for the financial distress prediction model

(2008) Wei-Sen Chen et al.   EXPERT SYSTEMS WITH APPLICATIONS

Predicting bank financial failures using neural networks, support vector machines and multivariate statistical methods: A comparative analysis in the sample of savings deposit insurance fund (SDIF) transferred banks in Turkey

(2008) Melek Acar Boyacioglu et al.   EXPERT SYSTEMS WITH APPLICATIONS

Evolving Least Squares Support Vector Machines for Stock Market Trend Mining

(2008) Lean Yu et al.   IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION

Exploratory Undersampling for Class-Imbalance Learning

(2008) Xu-Ying Liu et al.   IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS

Listed companies’ financial distress prediction based on weighted majority voting combination of multiple classifiers

(2007) Jie Sun et al.   EXPERT SYSTEMS WITH APPLICATIONS