A case-based reasoning with the feature weights derived by analytic hierarchy process for bankruptcy prediction

By Cheol-Soo Park, Ingoo Han. Expert systems with applications, 2002

299 citations recorded by the source corpus.

Subfields: Accounting, Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/7e5f71972e3c50777bfba3ddbe8813be8d89277c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Accounting

1. Hui Li
2. Jie Sun
3. Qinghua Huang
4. Kai-Yu He
5. Bernardete Ribeiro
6. Vadlamani Ravi
7. An Chen
8. Yannis Manolopoulos

In Artificial Intelligence

1. Hui Li
2. Jie Sun
3. Qinghua Huang
4. Kai-Yu He
5. Bernardete Ribeiro
6. Vadlamani Ravi
7. Yong Deng
8. An Chen

In Computational Theory and Mathematics

1. Bernardete Ribeiro
2. Vadlamani Ravi
3. An Chen
4. Bingyi Kang
5. Ningshan Chen
6. Jae H. Min
7. Qinghua Huang
8. Young-Chan Lee

Citations in the corpus

• Financial credit risk assessment: a recent review (2015, methodology): “In another study, CART was used to select predictors, then a classic LR model was constructed based on the dummy variables in accordance with the tree nodes (BrezigarMasten and Masten 2012).”
• Data Mining on Finance and Accounting: a Review of Current Research Trends (2004, background): “Park and Han (2002) in a CBR study developed a model to predict bankruptcy.”
• Soft computing system for bank performance prediction (2008, methodology): “Park and Han [37] employed k-nearest neighbour (k-NN) weighted with analytical hierarchy process (AHP) for predicting bankruptcy in Korean firms.”
• Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters (2005, background): “…the credit risk exposures of financial institutions, there is a strong need for sophisticated decision support systems backed by analytical tools to measure, monitor, manage, and control financial and operational risks as well as inefficiencies (Emel, Oral, Reisman, & Yolalan, 2003; Park & Han, 2002).”
• Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches (2014, methodology): “Most FDP researches used balanced samples [3,60,52,53,87,107,112,115].”
• A New Methodology of Multicriteria Decision-Making in Supplier Selection Based on -Numbers (2016, background): “Note that the theory about Z-number has not been figured out for its real application, yet the classic fuzzy set theory is relatively mature and has played much important role in the field of approximate reasoning [38, 39], fuzzy control [40, 41], group decision-making [42, 43], multiple criteria de”