文章基本信息

标题：Explanation vs Performance in Data Mining: A Case Study with Predicting Runaway Projects
本地全文：下载
作者：Tim MENZIES ; Osamu MIZUNO ; Yasunari TAKAGI 等
期刊名称：Journal of Software Engineering and Applications
印刷版ISSN：1945-3116
电子版ISSN：1945-3124
出版年度：2009
卷号：2
期号：4
页码：221-236
DOI：10.4236/jsea.2009.24030
出版社：Scientific Research Publishing
摘要：Often, the explanatory power of a learned model must be traded off against model performance. In the case of predict-ing runaway software projects, we show that the twin goals of high performance and good explanatory power are achievable after applying a variety of data mining techniques (discrimination, feature subset selection, rule covering algorithms). This result is a new high water mark in predicting runaway projects. Measured in terms of precision, this new model is as good as can be expected for our data. Other methods might out-perform our result (e.g. by generating a smaller, more explainable model) but no other method could out-perform the precision of our learned model.
关键词：Explanation; Data Mining; Runaway