Swarm intelligence based classification rule induction (CRI) framework for qualitative and quantitative approach: An application of bankruptcy prediction and credit risk analysis
| Autor: | P. Dhavachelvan, J. Uthayakumar, T. Vengattaraman |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
General Computer Science Computer science Swarm intelligence 02 engineering and technology Machine learning computer.software_genre lcsh:QA75.5-76.95 020901 industrial engineering & automation Credit risk analysis 0202 electrical engineering electronic engineering information engineering Bankruptcy prediction Classification Rule Induction business.industry Ant colony optimization algorithms Random forest Ant-miner Bankruptcy Multilayer perceptron Classification rule 020201 artificial intelligence & image processing Artificial intelligence Data mining lcsh:Electronic computers. Computer science business computer Credit risk |
| Zdroj: | Journal of King Saud University: Computer and Information Sciences, Vol 32, Iss 6, Pp 647-657 (2020) |
| ISSN: | 1319-1578 |
| Popis: | Bankruptcy prediction and credit risk analysis is one of the most significant problems in the field of accounting and financial decision making. Developing an effective classification rule induction (CRI) framework for bankruptcy prediction and credit risk analysis in appropriate time is essential to prevent the business communities from being bankrupt. Traditional statistical methods and artificial intelligence techniques play a major role to predict bankruptcy and credit risks. Most of the earlier research works were carried out on quantitative methods, while few studies have proposed on qualitative methods to improvise the performance of bankruptcy prediction models. The discovery of bankruptcy prediction in a qualitative way is an important task because it depends on the subjective knowledge of the experts. In this paper, a unified framework for qualitative and quantitative bankruptcy analysis using Ant Colony Optimization (ACO) based ant-miner algorithm is proposed. Three different natured datasets are used to present a trustworthy result. For this experiment, we have collected qualitative_bankruptcy dataset and benchmarked by UCI repository. The proposed method is successfully applied and the performance analysis prove that ant-miner method is better than existing classifiers namely Logistic Regression (LR), Multilayer Perceptron (MLP), Random Forest (RF) and Radial Basis Function (RBF) in terms of various performance analysis factors. Furthermore, the proposed ant-miner model is found to be a more suitable method for bankruptcy prediction when compared to other traditional statistical and artificial intelligence techniques. |
| Databáze: | OpenAIRE |
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