Detecting Mislabeled Data Using Supervised Machine Learning Techniques

A lot of data sets, gathered for instance during user experiments, are contaminated with noise. Some noise in the measured features is not much of a problem, it even increases the performance of many Machine Learning (ML) techniques. But for noise in the labels (mislabeled data) the situation is quite different, label noise deteriorates the performance of all ML techniques. The research question addressed in this paper is to what extent can one detect mislabeled data using a committee of supervised Machine Learning models. The committee under consideration consists of a Bayesian model, Random Forest, Logistic classifier, a Neural Network and a Support Vector Machine. This committee is applied to a given data set in several iterations of 5-fold Cross validation. If a data sample is misclassified by all committee members in all iterations (consensus) then it is tagged as mislabeled. This approach was tested on the Iris plant data set, which is artificially contaminated with mislabeled data. For this data set the precision of detecting mislabeled samples is 100% and the recall is approximately 5%. The approach was also tested on the Touch data set, a data set of naturalistic social touch gestures. It is known that this data set contains mislabeled data, but the amount is unknown. For this data set the proposed method achieved a precision of 70% and for almost all other tagged samples the corresponding touch gesture deviated a lot from the prototypical touch gesture. Overall the proposed method shows high potential for detecting mislabeled samples, but the precision on other data sets needs to be investigated.