Identification of label dependencies for multi-label classification Academic Article uri icon

abstract

  • The main feature distinguishing multi-label classification from a regular classification task is that a number of labels have to be predicted simultaneously. Thus, it is obviously important to exploit potential dependencies between labels. However, surprisingly only a few of the existing algorithms address this problem directly by identifying dependent labels explicitly from the dataset. In this research we propose a new approach for identification and modeling existing dependencies between labels. We define and develop an algorithm that, first, identifies dependencies among the labels, then divides the whole set of labels into several mutually exclusive subsets, and finally performs multilabel classification incorporating the discovered dependencies. In this paper we utilize the Chi-Square test for independence to identify interdependent labels. We then apply a combination of the …

publication date

  • June 25, 2010