Forecasting business failure in highly imbalanced distribution based on delay line reservoir - Rodan, Ali; Castillo-Valdivieso, Pedro A; Faris, Hossam; Al-Zoubi, Ala’ M; Mora, Antonio M; Jawazneh, H (ESANN 2018, 2020)
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.
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"
Keywords: Scopus(2)