On exploiting hierarchical label structure with pairwise classifiers

Entity Retrieval with Hierarchical Relevance Model, Exploiting the Structure of Tables and Learning Homepage Classifiers

This paper gives an overview of our work done for the TREC 2009 Entity track. We propose a hierarchical relevance retrieval model for entity ranking. In this model, three levels of relevance are examined which are document, passage and entity, respectively. The final ranking score is a linear combination of the relevance scores from the three levels. Furthermore, we exploit the structure of tab...

Exploiting Label Dependency for Hierarchical Multi-label Classification

Hierarchical multi-label classification is a variant of traditional classification in which the instances can belong to several labels, that are in turn organized in a hierarchy. Existing hierarchical multi-label classification algorithms ignore possible correlations between the labels. Moreover, most of the current methods predict instance labels in a “flat” fashion without employing the ontol...

On Pairwise Naive Bayes Classifiers

Class binarizations are effective methods for improving weak learners by decomposing multi-class problems into several two-class problems. This paper analyzes how these methods can be applied to a Naive Bayes learner. The key result is that the pairwise variant of Naive Bayes is equivalent to a regular Naive Bayes. This result holds for several aggregation techniques for combining the predictio...

Combining Pairwise Classifiers with Stacking

Pairwise classification is the technique that deals with multi-class problems by converting them into a series of binary problems, one for each pair of classes. The predictions of the binary classifiers are typically combined into an overall prediction by voting and predicting the class that received the largest number of votes. In this paper we try to generalize the voting procedure by replaci...

Improved Pairwise Coupling Classification with Correcting Classifiers