Author(s)

Tim MENZIES, Osamu MIZUNO, Yasunari TAKAGI, Tohru KIKUNO

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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.

Explanation, Data Mining, Runaway

MENZIES, T. , MIZUNO, O. , TAKAGI, Y. and KIKUNO, T. (2009) Explanation vs Performance in Data Mining: A Case Study with Predicting Runaway Projects. Journal of Software Engineering and Applications, 2, 221-236. doi: 10.4236/jsea.2009.24030.