*Detection and correction of mislabeled samples based on graph structure*

**DOI:**10.54647/isss12083 78 Downloads 3580 Views

**Author(s)**

**Abstract**

Machine learning trains and obtains learning models based on a large amount of training samples. Mislabeled training samples will affect the generalization/performance of the final predictive model. Some methods of detecting/correcting mislabeled samples such as graph-based methods, are proposed and used in machine learning to improve predictive models' generalization. However, these methods do not perform well for high-dimensional samples. In this paper, we present three algorithms for detecting/correcting mislabeled samples in high-dimensional feature space. First, we propose an improved high-dimensional detection algorithm: PCA-k-RNG. Next, we introduce a notion of ∈-scalar relative neighbourhood graph (∈-SRNG) and study its relationship with relative neighbourhood graph (RNG) and k-relative neighbourhood graph (k-RNG). Then, we give an alternative high-dimensional detection algorithm: PCA-∈-SRNG. After detecting mislabeled training samples, it is necessary to correct these mislabeled samples. Then we further propose a scalar-adapted correction algorithm: Fat location correction/deletion. Finally, we explore and validate our algorithms based on real datasets with high-dimensional features.

**Keywords**

high dimension, inaccurate supervision learning, mislabeled samples, relative neighbourhood graph (RNG), detection, correction.

**Cite this paper**

Junyan Li, Xinxing Wu,
Detection and correction of mislabeled samples based on graph structure
, *SCIREA Journal of Information Science and Systems Science*.
Volume 5, Issue 2, April 2021 | PP. 12-33.
10.54647/isss12083

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