A study of the robustness of KNN classifiers trained using soft labels

Authors:
Neamat El Gayar;Friedhelm Schwenker;Günther Palm
Affiliations:
Faculty of Computers and Information, Cairo University, Giza, Egypt;Department of Neural Information Processing, University of Ulm, Ulm, Germany;Department of Neural Information Processing, University of Ulm, Ulm, Germany
Venue:
ANNPR'06 Proceedings of the Second international conference on Artificial Neural Networks in Pattern Recognition
Year:
2006

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Abstract

Supervised learning models most commonly use crisp labels for classifier training. Crisp labels fail to capture the data characteristics when overlapping classes exist. In this work we attempt to compare between learning using soft and hard labels to train K-nearest neighbor classifiers. We propose a new technique to generate soft labels based on fuzzy-clustering of the data and fuzzy relabelling of cluster prototypes. Experiments were conducted on five data sets to compare between classifiers that learn using different types of soft labels and classifiers that learn with crisp labels. Results reveal that learning with soft labels is more robust against label errors opposed to learning with crisp labels. The proposed technique to find soft labels from the data, was also found to lead to a more robust training in most data sets investigated.