@article {450,
	title = {Study on college student credit evaluation and prediction based on RF algorithm},
	journal = {Journal of Digital Information Management},
	volume = {12},
	year = {2014},
	note = {Export Date: 10 July 2014},
	month = {2014},
	pages = {26 - 30},
	abstract = {In the increasingly serious environment of employment of college students, the college student credit evaluation model taking into account of the "basic personal situation", "on-campus situation" and "economic situation" is employed in this paper for the purpose of improving the quality of college student credit education more efficiently, and in addition, a college student credit prediction mechanism based on the improved RF algorithm is put forward, seen from the test result, the accuracy of college student credit prediction of the algorithm is relatively high, and capable to make student credit education more targeted.},
	keywords = {Credit evaluation, Prediction mode, Random forest algorithm},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84900433117\&partnerID=40\&md5=50b322efa1e0fc719d4244f770e8918a},
	author = {Mu, J. and Xu, L. and Pu, H.}
}
