Using Decision Trees and Soft Labeling to Filter Mislabeled Data
Xinchuan Zeng, Tony R. Martinez · Journal of Intelligent Systems · 2008
In this paper, we present a new noise filtering method, called soft decision tree noise filter (SDTNF), to identify and remove mislabeled data items in a data set.In this method, a sequence of decision trees is built from a data set, in which each data item is assigned a soft class label (in the form of a class probability vector).A modified decision tree algorithm is applied to adjust the soft class labeling during the tree building process.After each decision tree is built, the soft class label of each item in the data set is adjusted using the decision tree's predictions as the learning targets.In the next iteration, a new decision tree is built from a data set with the updated soft class labels.This tree building process repeats iteratively until the data set labeling converges.This procedure provides a mechanism to gradually modify and correct mislabeled items.The process is applied in SDTNF as a filtering method by identifying data items whose classes have been relabeled as mislabeled data by decision trees.The performance of SDTNF is evaluated using 16 data sets drawn from the UCI data repository.The results show that SDTNF is capable of identifying a substantial amount of noise for most of the tested data sets and significantly improving performance of nearest neighbor classifiers at a wide range of noise levels.We also compare SDTNF to the consensus and majority voting methods proposed by Brodley and Friedl (1996, 1999) for noise filtering.The results show that in terms of filtering mislabeled data and keeping non-mislabeled data, the SDTNF filtering capability is more efficient and more balanced than these two methods.The results also show that the filtering capability of SDTNF can significantly improve the performance of nearest neighbor classifiers, especially at high