Sanz, Jose Antonio and Bernardo, Dario and Herrera, Francisco and Bustince, Humberto and Hagras, Hani (2015) A Compact Evolutionary Interval-Valued Fuzzy Rule-Based Classification System for the Modeling and Prediction of Real-World Financial Applications With Imbalanced Data. IEEE Transactions on Fuzzy Systems, 23 (4). pp. 973-990. DOI https://doi.org/10.1109/tfuzz.2014.2336263
Sanz, Jose Antonio and Bernardo, Dario and Herrera, Francisco and Bustince, Humberto and Hagras, Hani (2015) A Compact Evolutionary Interval-Valued Fuzzy Rule-Based Classification System for the Modeling and Prediction of Real-World Financial Applications With Imbalanced Data. IEEE Transactions on Fuzzy Systems, 23 (4). pp. 973-990. DOI https://doi.org/10.1109/tfuzz.2014.2336263
Sanz, Jose Antonio and Bernardo, Dario and Herrera, Francisco and Bustince, Humberto and Hagras, Hani (2015) A Compact Evolutionary Interval-Valued Fuzzy Rule-Based Classification System for the Modeling and Prediction of Real-World Financial Applications With Imbalanced Data. IEEE Transactions on Fuzzy Systems, 23 (4). pp. 973-990. DOI https://doi.org/10.1109/tfuzz.2014.2336263
Abstract
The current financial crisis has stressed the need to obtain more accurate prediction models in order to decrease risk when investing money on economic opportunities. In addition, the transparency of the process followed to make the decisions in financial applications is becoming an important issue. Furthermore, there is a need to handle real-world imbalanced financial datasets without using sampling techniques that might introduce noise in the used data. In this paper, we present a compact evolutionary interval-valued fuzzy rule-based classification system, which is based on interval-valued fuzzy rule-based classification system with tuning and rule selection (IVTURS FA RC-HD ) for the modeling and prediction of real-world financial applications. This proposed system allows obtaining good prediction accuracies using a small set of short fuzzy rules implying a high degree of interpretability of the generated linguistic model. Furthermore, the proposed system deals with the financial imbalanced datasets with no need for any preprocessing or sampling method and, thus, avoiding the accidental introduction of noise in the data used in the learning process. The system is also provided with a mechanism to handle examples that are not covered by any fuzzy rule in the generated rule base. To test the quality of our proposal, we will present an experimental study including 11 real-world financial datasets. We will show that the proposed system outperforms the original C4.5 decision tree, type-1, and interval-valued fuzzy counterparts that use the synthetic minority oversampling technique (SMOTE) to preprocess data and the original FURIA, which is a fuzzy approximative classifier. Furthermore, the proposed method enhances the results achieved by the cost-sensitive C4.5, and it gives competitive results when compared with FURIA using SMOTE, while our proposal avoids preprocessing techniques, and it provides interpretable models that allow obtaining more accurate results.
Item Type: | Article |
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Uncontrolled Keywords: | Evolutionary algorithms; financial applications; interval-valued fuzzy rule-based classification systems; interval-valued fuzzy sets (IVFSs) |
Subjects: | H Social Sciences > HG Finance Q Science > Q Science (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 24 Oct 2014 10:44 |
Last Modified: | 16 May 2024 16:32 |
URI: | http://repository.essex.ac.uk/id/eprint/10959 |
Available files
Filename: IEEETFSsanz.pdf