Using semantic relatedness to improve the evaluation of multi-label classifiers
Christophe Deloo · Research Repository (Delft University of Technology) · 2013
Different evaluation techniques can be used in order to assess the quality of a multi-label classification task. The most commonly used performance measures in classification are the classic information retrieval notions of precision and re-call. These measures assume independence between the target labels meaning that the assignment of a label to an item can either be evaluated as correct or incorrect and not somewhere in between. The drawback of these binary evalu-ation measures is that they do not take into account the difference in semantics between the concepts depicted by the labels. For example, an item classified as plant instead of flower would be considered as wrong although the concepts are semantically related and a flower is a specialisation of a plant. A way of im-proving the evaluation of a classification would be to also consider the degree of semantic relatedness between concepts. The work in this thesis focuses on improving the evaluation of a real-world multi-label classification task by using the notion of semantic relatedness. The