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A compact evolutionary interval-valued fuzzy rule-based classification system for the modeling and prediction of real-world financial applications with imbalanced data

Identificadores del recurso

J. A. Sanz, D. Bernardo, F. Herrera, H. Bustince and H. Hagras, "A Compact Evolutionary Interval-Valued Fuzzy Rule-Based Classification System for the Modeling and Prediction of Real-World Financial Applications With Imbalanced Data," in IEEE Transactions on Fuzzy Systems, vol. 23, no. 4, pp. 973-990, Aug. 2015. doi: 10.1109/TFUZZ.2014.2336263

1063-6706

https://hdl.handle.net/2454/17689

10.1109/TFUZZ.2014.2336263

Procedència

(Academica-e: repositorio digital de la Universidad Pública de Navarra)

Fitxa

Títol:: A compact evolutionary interval-valued fuzzy rule-based classification system for the modeling and prediction of real-world financial applications with imbalanced data
Tema:: Financial applications; Interval-valued fuzzy sets; Interval-valued fuzzy rule-based classification systems; Evolutionary algorithms
Descripció:: The current financial crisis has stressed the need of obtaining more accurate prediction models in order to decrease the 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 the real-world imbalanced financial data sets without using sampling techniques which 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 IVTURSFARC-HD (Interval-Valued fuzzy rule-based classification system with TUning and Rule Selection) [22]), for the modeling and prediction of real-world financial applications. This proposed system allows obtaining good predictions 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 eleven 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 which use the SMOTE sampling technique 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 pre-processing techniques and it provides interpretable models that allow obtaining more accurate results.; This work was supported in part by the Spanish Ministry of Science and Technology under Project TIN2011-28488 and Project TIN2013-40765.
Idioma:: English
Relació:: IEEE Transactions on Fuzzy Systems; info:eu-repo/grantAgreement/MICINN//TIN2011-28488/ES/; info:eu-repo/grantAgreement/MINECO//TIN2013-40765-P/ES/; https://dx.doi.org/10.1109/TFUZZ.2014.2336263
Autor/Productor:: 0000-0002-1427-9909; Bernardo, Darío; Herrera, Francisco; 0000-0002-1279-6195; Hagras, Hani
Editor:: IEEE
Otros colaboradores/productores:: Universidad Pública de Navarra. Departamento de Automática y Computación; Nafarroako Unibertsitate Publikoa. Automatika eta Konputazioa Saila
Drets:: © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.; Acceso abierto / Sarbide irekia; info:eu-repo/semantics/openAccess
Data:: 2014
Tipo de recurso:: info:eu-repo/semantics/article; info:eu-repo/semantics/acceptedVersion
Format:: application/pdf

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