Real-world data sets often contain mislabelled entities. This can be particularly problematic if the data set is being used by a supervised classification algorithm at its learning phase. In this case, the accuracy of this classification algorithm, when applied to unlabelled data, is likely to suffer considerably. In this paper, we introduce a clustering-based method capable of reducing the number of mislabelled entities in data sets. Our method can be summarised as follows: (i) cluster the data set; (ii) select the entities that have the most potential to be assigned to correct clusters; (iii) use the entities of the previous step to define the core clusters and map them to the labels using a confusion matrix; (iv) use the core clusters and our cluster membership criterion to correct the labels of the remaining entities. We perform numerous experiments to validate our method empirically using k-nearest neighbour classifiers as a benchmark. We experiment with both synthetic and real-world data sets with different proportions of mislabelled entities. Our experiments demonstrate that the proposed method produces promising results. Thus, it could be used as a preprocessing data correction step of a supervised machine learning algorithm.
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BM thanks the Laboratory for Decision Choice and Analysis of the National Research University Higher School of Economics Moscow RF for partially supporting his work in the framework of the HSE University Basic Research Program funded by the Russian Academic Excellence Project ‘5-100’.
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de Amorim, R.C., Makarenkov, V. & Mirkin, B. Core Clustering as a Tool for Tackling Noise in Cluster Labels. J Classif 37, 143–157 (2020). https://doi.org/10.1007/s00357-019-9303-4
- Label noise
- Core clustering
- Minkowski distance