Preference Learning for Category-Ranking based Interactive Text Categorization

Category Ranking is a variant of the multi-label classification problem, in which, rather than performing a (hard) assignment to an object of categories from a predefined set, we rank all categories according to their estimated "degree of suitability" to the object. Category ranking has many applications, all pertaining to "interactive" classification contexts in which the system, rather than taking a final categorization decision, is simply required to support a human expert who is in charge of taking this decision. Despite its high applicative potential in information retrieval applications, and in text categorization in particular, category ranking has mainly been tackled by standard text categorization methods. In this paper, we take a radically different stand to category ranking, i.e. one in which supervision is provided to the learner not in the standard form of labels attached to training documents, but in the form of preferences of type "category c is to be preferred to category c2 for document d". We apply to this problem a recently proposed, very general model for preferential learning, and show, through experiments performed on the standard Reuters-21578 benchmark, that this largely outperforms support vector machines, the learning method which has up to now proved the best-performing one in text categorization comparative experiments.