Robust Decision Trees: Removing Outliers from Databases

George H. John, Stanford University

Finding and removing outliers is an important problem in data mining. Errors in large databases can be extremely common, so an important property of a data mining algorithm is robustness with respect to errors in the database. Most sophisticated methods in machine learning address this problem to some extent, but not fully, and can be improved by addressing the problem more directly. In this paper we examine C4.5, a decision tree algorithm that is already quite robust - few algorithms have been shown to consistently achieve higher accuracy. C4.5 incorporates a pruning scheme that partially addresses the outlier removal problem. In our Robust-C4.5 algorithm we extend the pruning method to fully remove the effect of outliers, and this results in improvement on many databases.


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