A data mining approach to the prediction of corporate failure

Financial failure prediction using efficiency as a predictor

“…These selected variables are usually viewed as the causes of the corporate bankruptcy (Laitinen & Laitinen, 2000). Number of variables selected by different researchers or under different problem background are different, ranging from 4 variables (Lin & McClean, 2001) to 13 variables (Min, Lee, & Han, 2006), as we review the literature.…”

Predicting business failure using forward ranking-order case-based reasoning

“…With the fast development of computing techniques in the last century, intelligent methods begin to take a key role in BFP. The applied intelligent methods include decision trees (DT) (Frydman et al, 1985;Sun & Li, 2008a;Gepp, Kumar, & Bhattacharya, 2010), neural networks (Odom & Sharda, 1990;Serrano-Cinca, 1996;Wilson & Sharda, 1994), CBR (Bryant, 1997;Jo et al, 1997;Li & Sun, 2008, 2009a, 2009bLin & McClean, 2001;Li, Sun, & Sun, 2009;Park & Han, 2002;Yip, 2004;Yip & Deng, 2003), support vector machines (SVM) (Hua, Wang, & Xu, 2007;Min & Lee, 2005;Shin, Lee, & Kim, 2005;Hardle, Lee, & Schafer, 2009;Yeh, Chi, & Hsu, 2010). Most of these researches demonstrate the applicability of various intelligent methods in BFP.…”

“…They may effectively avail themselves of different information in training data. In order to take advantage of more than two predictive methods to generate better predictive performance, combined methods, a special hybrid means, call attention of researchers in the area (Cho, Kim, & Bae, 2009;Jo & Han, 1996;Lin & McClean, 2001;Nanni & Lumini, 2009;Ravi, Kurniawan, & Thai, 2008;Sun & Li, 2008b;Tsai & Wu, 2008).…”

“…Various predictive models (Kumar & Ravi, 2007), such as statistical ones (Altman, 1968;Beaver, 1966;Martin, 1977;Ohlson, 1980), intelligent ones (Cielen, Peeters, & Vanhoof, 2004;Dimitras, Slowinski, & Susmaga, 1999;Frydman, Altman, & Kao, 1985;Hu & Ansell, 2007;Jo, Han, & Lee, 1997;McKee & Lensberg, 2002;Min & Lee, 2005;Odom & Sharda, 1990;Sun & Li, 2008a;Sun & Shenoy, 2007;Tay & Shen, 2002;Tseng & Hu, 2010), and ensembles (Jo & Han, 1996;Lin & McClean, 2001;Sun & Li, 2008b) have been developed.…”

Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: a survey

“…In both works, improvement in prediction accuracy was reported, when compared with the best single model. An MLP, LR, LDA, and C5.0 decision tree were combined into the weighted voting ensemble developed by Lin and McClean (2001). The weights were proportional to the prediction accuracy of the ensemble members estimated on the training data set.…”