TITLE:
Explanation vs Performance in Data Mining: A Case Study with Predicting Runaway Projects
AUTHORS:
Tim MENZIES, Osamu MIZUNO, Yasunari TAKAGI, Tohru KIKUNO
KEYWORDS:
Explanation, Data Mining, Runaway
JOURNAL NAME:
Journal of Software Engineering and Applications,
Vol.2 No.4,
November
27,
2009
ABSTRACT: 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.