Paper Info
Title
Combining cluster analysis with classifier ensembles to predict financial distress
Abstract
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.
Year
DOI
Venue
2014
10.1016/j.inffus.2011.12.001
Information Fusion
Keywords
Field
DocType
financial institution,classifier ensemble,combining cluster analysis,hybrid classifier,financial decision support,financial crisis,financial decision-making,ii error,clustering technique,financial distress model,self-organizing maps,machine learning
Data mining,Decision tree,Computer science,Artificial intelligence,Artificial neural network,Cluster analysis,Classifier (linguistics),Business failure,Pattern recognition,Decision support system,Bankruptcy prediction,Probabilistic classification,Machine learning
Journal
Volume
ISSN
Citations 
16,
1566-2535
37
PageRank 
References 
Authors
0.88
32
1
Name
Order
Citations
PageRank
Chih-fong Tsai1125554.93