Forecasting business failure in highly imbalanced distribution based on delay line reservoir
Autor:
Rodan, Ali
; Castillo-Valdivieso, Pedro A
; Faris, Hossam
; Al-Zoubi, Ala’ M
; Mora, Antonio M
; Jawazneh, H
Fecha:
2018Palabra clave:
Revista / editorial:
ESANN 2018Tipo de Ítem:
conferenceObjectResumen:
Bankruptcy is a critical financial problem that affects a high number of companies around the world. Thus, in recent years an increasing number of researchers have tried to solve it by applying different machine-learning models as powerful tools for the different economical agents related to the company. In this work, we propose the use of a simple deterministic delay line reservoir (DLR) state space by combining it with three popular classification algorithms (J48, k-NN, and MLP) as an efficient and accurate solution to the bankruptcy prediction problem. The proposed approach is evaluated on a real world dataset collected from Spanish companies. Obtained results show that the proposed models have a higher predictive ability than traditional classification approaches (without DLR reservoir state), resulting in a suitable and efficient alternative approach to solve this complex problem.
Descripción:
Ponencia de la conferencia "26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2018; Bruges; Belgium; 25 April 2018 through 27 April 2018"
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