Multilayer feedforward networks are universal approximators
Neural Networks
Neural networks in business: techniques and applications for the operations researcher
Computers and Operations Research - Neural networks in business
Statistical and neural classifiers: an integrated approach to design
Statistical and neural classifiers: an integrated approach to design
Data visualisation and manifold mapping using the ViSOM
Neural Networks - New developments in self-organizing maps
An introduction to variable and feature selection
The Journal of Machine Learning Research
Credit rating analysis with support vector machines and neural networks: a market comparative study
Decision Support Systems - Special issue: Data mining for financial decision making
Optimization-based feature selection with adaptive instance sampling
Computers and Operations Research
Using neural network ensembles for bankruptcy prediction and credit scoring
Expert Systems with Applications: An International Journal
Genetic algorithm-based feature set partitioning for classification problems
Pattern Recognition
A comparison of supervised and unsupervised neural networks in predicting bankruptcy of Korean firms
Expert Systems with Applications: An International Journal
Building credit scoring models using genetic programming
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
Bankruptcy prediction with neural logic networks by means of grammar-guided genetic programming
Expert Systems with Applications: An International Journal
Failure prediction with self organizing maps
Expert Systems with Applications: An International Journal
Bankruptcy prediction for credit risk using neural networks: A survey and new results
IEEE Transactions on Neural Networks
Nonlinear fuzzy robust PCA algorithms and similarity classifier in bankruptcy analysis
Expert Systems with Applications: An International Journal
Understanding consumer heterogeneity: A business intelligence application of neural networks
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
Using partial least squares and support vector machines for bankruptcy prediction
Expert Systems with Applications: An International Journal
Search and analysis of bankruptcy cause by classification network
MEDI'11 Proceedings of the First international conference on Model and data engineering
Two credit scoring models based on dual strategy ensemble trees
Knowledge-Based Systems
Dimensionality reduction and main component extraction of mass spectrometry cancer data
Knowledge-Based Systems
Simple instance selection for bankruptcy prediction
Knowledge-Based Systems
A hybrid device for the solution of sampling bias problems in the forecasting of firms' bankruptcy
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Determinants of intangible assets value: The data mining approach
Knowledge-Based Systems
Efficient classifiers for multi-class classification problems
Decision Support Systems
A window of opportunity: Assessing behavioural scoring
Expert Systems with Applications: An International Journal
Genetic algorithms in feature and instance selection
Knowledge-Based Systems
Bi-objective feature selection for discriminant analysis in two-class classification
Knowledge-Based Systems
Going-concern prediction using hybrid random forests and rough set approach
Information Sciences: an International Journal
Robust feature selection based on regularized brownboost loss
Knowledge-Based Systems
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For many corporations, assessing the credit of investment targets and the possibility of bankruptcy is a vital issue before investment. Data mining and machine learning techniques have been applied to solve the bankruptcy prediction and credit scoring problems. As feature selection is an important step to select more representative data from a given dataset in data mining to improve the final prediction performance, it is unknown that which feature selection method is better. Therefore, this paper aims at comparing five well-known feature selection methods used in bankruptcy prediction, which are t-test, correlation matrix, stepwise regression, principle component analysis (PCA) and factor analysis (FA) to examine their prediction performance. Multi-layer perceptron (MLP) neural networks are used as the prediction model. Five related datasets are used in order to provide a reliable conclusion. Regarding the experimental results, the t-test feature selection method outperforms the other ones by the two performance measurements.