U. M. Fayyad and I. H. Irani, “Multi-interval discretiza-tion of continuous-valued attributes for classification learning,” in Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, pp. 1022–1027, 1993.

has been cited by the following article:

• 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.