id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_j5q5tirszvanva633mw725nnu4	C ONG	Building credit scoring models using genetic programming	2005	7	.pdf	application/pdf	4369	501	63	Credit scoring models have been widely studied in the areas of statistics, machine learning, and artificial intelligence (AI). approaches such as artificial neural networks (ANNs), rough sets, or decision trees have been proposed to increase the accuracy of credit model should be proposed to significantly improving the accuracy of the credit scoring mode. Two numerical examples will be employed here to compare the error rate to other credit scoring models including the ANN, decision trees, rough sets, and logistic regression. Keywords: Credit scoring; Artificial neural network (ANN); Decision trees; Genetic programming (GP); Rough sets The advantage of the induction based approaches (e.g. rough sets and decision trees) is that it can provide the Cho, & Kim, 2000; Beynon & Peel, 2001; Dimitras, set includes Australian credit scoring data with 307 The comparison of the credit scoring models in Australian data set The comparison of the credit scoring models in German data set	./cache/work_j5q5tirszvanva633mw725nnu4.pdf	./txt/work_j5q5tirszvanva633mw725nnu4.txt