Bankruptcy prediction using neural networks
Decision Support Systems - Special issue on neural networks for decision support
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Genetic Programming for Feature Discovery and Image Discrimination
Proceedings of the 5th International Conference on Genetic Algorithms
Rough Set Based Data Exploration Using ROSE System
ISMIS '99 Proceedings of the 11th International Symposium on Foundations of Intelligent Systems
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Loan payment prediction using rough sets
MATH'08 Proceedings of the American Conference on Applied Mathematics
Integrated Computer-Aided Engineering
Credit Risk Assessment Model of Commercial Banks Based on Fuzzy Neural Network
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
A Genetic Programming Approach for Bankruptcy Prediction Using a Highly Unbalanced Database
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
Bankruptcy prediction with neural logic networks by means of grammar-guided genetic programming
Expert Systems with Applications: An International Journal
An evolution of geometric structures algorithm for the automatic classification of HRR radar targets
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
A SOM and GP tool for reducing the dimensionality of a financial distress prediction problem
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
Expert Systems with Applications: An International Journal
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Hi-index | 0.01 |
Prediction of non-life insurance companies insolvency has arised as an important problem in the field of financial research, due to the necessity of protecting the general public whilst minimizing the costs associated to this problem, such as the effects on state insurance guaranty funds or the responsibilities for management and auditors. Most methods applied in the past to predict business failure in non-life insurance companies are traditional statistical techniques, which use financial ratios as explicative variables. However, these variables do not usually satisfy statistical assumptions, what complicates the application of the mentioned methods. Emergent statistical learning methods like neural networks or SVMs provide a successful approach in terms of error rate, but their character of black-box methods make the obtained results difficult to be interpreted and discussed. In this paper, we propose an approach to predict insolvency of non-life insurance companies based on the application of genetic programming (GP). GP is a class of evolutionary algorithms, which operates by codifying the solution of the problem as a population of LISP trees. This type of algorithm provides a diagnosis output in the form of a decision tree with given functions and data. We can treat it like a computer program which returns an answer depending on the input, and, more importantly, the tree can potentially be inspected, interpreted and re-used for different data sets. We have compared the performance of GP with other classifiers approaches, a Support Vector Machine and a Rough Set algorithm. The final purpose is to create an automatic diagnostic system for analysing non-insurance firms using their financial ratios as explicative variables.