Credit scoring using the hybrid neural discriminant technique

By Tian-Shyug Lee, Chih-Chou Chiu, Chi-Jie Lu, I-Fei Chen. Expert systems with applications, 2002

389 citations recorded by the source corpus.

Subfields: Accounting, Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/a90cc0535fc5f9a46a07707c30c9f6339e3ce702

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Citations in the corpus

Credit Scoring, Statistical Techniques and Evaluation Criteria: A Review of the Literature (2011, methodology): “Meanwhile, comparisons between traditional and advanced statistical techniques have been investigated too (Abdou & Pointon, 2009; Abdou et al. 2009; Lee & Chen 2005; Ong et al, 2005; Zekic-Susac et al, 2004; Malhotra & Malhotra, 2003; Lee et al, 2002; Fritz & Hosemann, 2000).”
The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients (2009, methodology): “Lee et al. (2002) explored the performance of credit scoring by integrating the backpropagation neural networks with the traditional discriminant analysis approach.”
Mining the customer credit using classification and regression tree and multivariate adaptive regression splines (2006, background): “…et al. (1999), Deng (1993), Desai et al. (1996), Glorfeld (1996), Glorfeld and Hardgrave (1996), Jagielska and Jaworski (1996), Jensen (1992), Lee et al. (2002), Piramuthu (1999), Piramuthu et al. (1994), Richeson et al. (1994), Torsun (1996), andWest (2000)in handling credit scoring…”
A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems (2010, methodology): “[182] Credit scoring BPNN with LDA Conventional BPNN, LDA and LR Hybrid model performs better”
Artificial neural networks in business: Two decades of research (2016, background): “Throughout the investigated period authors have applied artificial neural networks to various problems including credit scoring [161, 258, 259], financial analysis [52, 186], or stock performance prediction [89, 162, 166].”
Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters (2005, methodology): “Armed with these models, quantitative risk management systems can thus provide the financial institutions early warning signals for potential business failures of the clients (Chen & Huang, 2003; Lee, Chiu, Lu, & Chen, 2002; Lopez & Saidenberg, 2000; West, 2000).”
Classification methods applied to credit scoring: A systematic review and overall comparison (2016, methodology): “Some authors used a variable selection procedure in their papers such as Lee et al. (2002); Verstraeten and Van Den Poel (2005); Abdou et al. (2008); Chen and Li (2010); Marques et al. (2012b).”
A new fuzzy support vector machine to evaluate credit risk (2005, methodology): “References [14], [26], and [32] provide a probability framework for understanding SVM or least square SVM.”
Recent developments in consumer credit risk assessment (2007, methodology): “Yet others have used a stepwise routine in LDA to select the variables for inclusion in a NN (Lee et al., 2002)).”
Credit risk assessment with a multistage neural network ensemble learning approach (2008, neutral): “Recent examples are Neural Discriminant Technique ( Lee, Chiu, Lu, & Chen, 2002 ), neuro-fuzzy (Malhotra & Malhotra, 2002; Piramuthu, 1999) and fuzzy SVM (Wang et al., 2005).”
A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines (2005, methodology): “…network’s topology, not easy to identify the relative importance of potential input variables, and certain interpretive difficulties and hence has limited its applicability in handling general classification and credit scoring problems (Craven & Shavlik, 1997; Lee et al., 2002; Piramuthu, 1999).”
Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: a survey (2010, methodology): “A similar approach was also taken by Lee et al. (2002).”
Building credit scoring models using genetic programming (2005, background): “Since an improvement in accuracy of a fraction of a percent might translate into significant savings, a more sophisticated model should be proposed to significantly improving the accuracy of the credit scoring mode.”