ISSN 1817-2172, рег. Эл. № ФС77-39410, ВАК

Differential Equations and Control Processes
(Differencialnie Uravnenia i Protsesy Upravlenia)

An Experimental Study of the Machine Learning Algorithms Response to Data Labelling Errors


Viacheslav Anatolievich Diuk

Doctor of Technical Sciences,
Principal Researcher of the Institute for Transport
Problems of the Russian Academy of Sciences (IPT RAS)
Russia, 199178, St. Petersburg, 12-th Line VO, 13.


There are authoritative opinions that data labeling is today the most important element in the procedure for creating AI systems based on machine learning methods. At the same time, in particular with crowdsourcing, there is a serious problem of inaccurate data labeling. The materials of this article complement the well-known approaches to solving this problem by studying the reaction to inaccurate data labeling of some popular machine learning methods. These are naive Bayesian classifier, three-layer perceptron, nearest neighbor method (KNN), decision trees, random forest, logistic regression, support vector machine (SVM). We trained algorithms on copies of specially generated data with different proportions of labeling errors and then tested them on data with accurate labeling. Based on the results of the experiment on data simulating a simple and complex structure of two classes of multidimensional objects, the phenomenon of a relatively weak dependence of the accuracy of the KNN and SVM classification models on the labeling errors of the training sample was demonstrated. In conditions of inaccurate data labeling, the KNN algorithm is more preferable. It is less complicated, has fewer adjustable parameters, is free from a priori assumptions about the data structure, is resistant to anomalous outliers, and is interpretable. In addition, this method has significant potential for further theoretical and practical development based on the approach associated with the construction of context-dependent local metrics.



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