Multilabel Ranking with Inconsistent Rankers

While most existing multilabel ranking methods assume the availability of a single objective label ranking for each instance in the training set, this paper deals with a more common case where subjective inconsistent rankings from multiple rankers are associated with each instance. The key idea is to learn a latent preference distribution for each in-stance. The proposed method mainly includes two steps. The first step is to generate a common preference distribu-tion that is most compatible to all the personal rankings. The second step is to learn a mapping from the instances to the preference distributions. The proposed preference dis-tribution learning (PDL) method is applied to the problem of multilabel ranking for natural scene images. Experimen-tal results show that PDL can effectively incorporate the information given by the inconsistent rankers, and perform remarkably better than the compared state-of-the-art multi-label ranking algorithms. 1