Online learning the consensus of multiple correspondences between sets.

When several subjects solve the assignment problem of two sets, differences on the correspondences computed by these subjects may occur. These differences appear due to several factors. For example, one of the subjects may give more importance to some of the elements’ attributes than another subject. Another factor could be that the assignment problem is computed through a suboptimal algorithm and different non-optimal correspondences can appear. In this paper, we present a consensus methodology to deduct the consensus of several correspondences between two sets. Moreover, we also present an online learning algorithm to deduct some weights that gauge the impact of each initial correspondence on the consensus. In the experimental section, we show the evolution of these parameters together with the evolution of the consensus accuracy. We observe that there is a clear dependence of the learned weights with respect to the quality of the initial correspondences. Moreover, we also observe that in the first iterations of the learning algorithm, the consensus accuracy drastically increases and then stabilises

Cross-domain polarity classification using a knowledge-enhanced meta-classifier

"NOTICE: this is the author’s version of a work that was accepted for publication in Knowledge-Based Systems. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in KNOWLEDGE-BASED SYSTEMS [Volume 86, September 2015, Pages 46–56] DOI http://dx.doi.org/10.1016/j.knosys.2015.05.020In this paper, we propose the use of meta-learning to combine and enrich those approaches by adding also other knowledge-based features. In addition to the aforementioned classical approaches, our system uses the BabelNet multilingual semantic network to generate features derived from word sense disambiguation and vocabulary expansion. Experimental results show state-of-theart performance on single and cross-domain polarity classification.This research has been carried out in the framework of the European Commission WIQ-EI IRSES (No. 269180) and DIANA-APPLICATIONS - Finding Hidden Knowledge in Texts: Applications (TIN2012-38603-C02-01) projects. This research is partially funded by the national project ACOGEUS (TIN2012-38536-C03-02) and the regional project AORESCU (P11-TIC-7684 MO). We thank Juan M. Cotelo and Luis A. Leiva for their support and comments.Franco-Salvador, M.; Cruz, FL.; Troyano Jiménez, JA.; Rosso, P. (2015). Cross-domain polarity classification using a knowledge-enhanced meta-classifier. Knowledge-Based Systems. 86:46-56. doi:10.1016/j.knosys.2015.05.020S46568