IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural network applications in finance: a review and analysis of literature (1990-1996)
Information and Management
ACM Computing Surveys (CSUR)
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
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
Neural network ensemble strategies for financial decision applications
Computers and Operations Research
Expert Systems with Applications: An International Journal
Credit scoring with a data mining approach based on support vector machines
Expert Systems with Applications: An International Journal
Using neural network ensembles for bankruptcy prediction and credit scoring
Expert Systems with Applications: An International Journal
Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks
Decision Support Systems
Feature selection in bankruptcy prediction
Knowledge-Based Systems
Business failure prediction using hybrid2 case-based reasoning (H2CBR)
Computers and Operations Research
Computational Statistics & Data Analysis
Credit rating by hybrid machine learning techniques
Applied Soft Computing
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
Hybrid mining approach in the design of credit scoring models
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
A survey of multiple classifier systems as hybrid systems
Information Fusion
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The ability to accurately predict business failure is a very important issue in financial decision-making. Incorrect decision-making in financial institutions is very likely to cause financial crises and distress. Bankruptcy prediction and credit scoring are two important problems facing financial decision support. As many related studies develop financial distress models by some machine learning techniques, more advanced machine learning techniques, such as classifier ensembles and hybrid classifiers, have not been fully assessed. The aim of this paper is to develop a novel hybrid financial distress model based on combining the clustering technique and classifier ensembles. In addition, single baseline classifiers, hybrid classifiers, and classifier ensembles are developed for comparisons. In particular, two clustering techniques, Self-Organizing Maps (SOMs) and k-means and three classification techniques, logistic regression, multilayer-perceptron (MLP) neural network, and decision trees, are used to develop these four different types of bankruptcy prediction models. As a result, 21 different models are compared in terms of average prediction accuracy and Type I & II errors. By using five related datasets, combining Self-Organizing Maps (SOMs) with MLP classifier ensembles performs the best, which provides higher predication accuracy and lower Type I & II errors.