%0 Journal Article
%T Explanation vs Performance in Data Mining: A Case Study with Predicting Runaway Projects
%A Tim MENZIES
%A Osamu MIZUNO
%A Yasunari TAKAGI
%A Tohru KIKUNO
%J Journal of Software Engineering and Applications
%P 221-236
%@ 1945-3124
%D 2009
%I Scientific Research Publishing
%R 10.4236/jsea.2009.24030
%X 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.
%K Explanation
%K Data Mining
%K Runaway
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=884