The integrated methodology of rough set theory and artificial neural network for business failure prediction

By Byeong Seok Ahn, S. S. Cho, C. Y. Kim. 2000

360 citations recorded by the source corpus.

Subfields: Accounting, Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/caa1ee6de30df4faed3d25e77731f1a2749396bf

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Citations in the corpus

Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review (2007, methodology): “[1] Firms Korea, KIS (Korea Information Service)* 2400 DA, BPNN, RNN1, RNN2, Hybrid I, Hybrid II 1994–1997”
Artificial neural networks in business: Two decades of research (2016, neutral): “Financial distress & bankruptcy (76) [8, 13, 16, 17, 30, 31, 38, 40, 60, 62, 64, 65, 74, 77, 78, 86, 90, 97, 98, 107, 128, 129, 147, 145, 158, 159, 167, 168, 176, 177, 179, 180, 181, 183, 185, 189, 193, 194, 201, 203, 204, 212, 213, 218, 220, 223, 227, 243, 253, 254, 257, 266, 274, 288, 308, 318, 32”
Soft computing system for bank performance prediction (2008, methodology): “[1] proposed hybrid models combining rough sets and MLFF-BP for bankruptcy prediction in Korean firms.”
Building credit scoring models using genetic programming (2005, methodology): “Although these induction methods have been well developed and successfully used in credit scoring problems ( Ahn, Cho, & Kim, 2000; Beynon & Peel, 2001; Dimitras, Slowinski, Susmaga, & Zopounidis, 1999), the main problem of induction based methods is the ability of forecasting.”
Prediction of financial distress: An empirical study of listed Chinese companies using data mining (2015, methodology): “…classifiers based on majority voting Bose and Pal (2006) Yes Yes No No Yes No No No Survived and failed click-and-mortar companies Neural networks Ahn, Cho, and Kim (2000) Yes No No No No Yes No No Korean firms selected from Korea Information Service Hybrid model integrating rough sets and…”
Deep Learning-Based Corporate Performance Prediction Model Considering Technical Capability (2017, methodology): “Ahn et al. (2000) conducted a study to predict the bankruptcy of ﬁrms using a neural network algorithm [4].”
Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: a survey (2010, methodology): “2007) and rough sets (Zhou and Tian 2007; Ahn et al. 2000; McKee and Lensberg 2002) are the two most popular approaches to feature selection in hybrid and ensemble-based techniques for bankruptcy prediction.”