Multilayer perceptron for label ranking

Authors:
Geraldina Ribeiro;Wouter Duivesteijn;Carlos Soares;Arno Knobbe
Affiliations:
Faculdade de Economia, Universidade do Porto, Portugal;LIACS, Leiden University, The Netherlands;INESC TEC, Universidade do Porto, Portugal;LIACS, Leiden University, The Netherlands
Venue:
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Year:
2012

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Abstract

Label Ranking problems are receiving increasing attention in machine learning. The goal is to predict not just a single value from a finite set of labels, but rather the permutation of that set that applies to a new example (e.g., the ranking of a set of financial analysts in terms of the quality of their recommendations). In this paper, we adapt a multilayer perceptron algorithm for label ranking. We focus on the adaptation of the Back-Propagation (BP) mechanism. Six approaches are proposed to estimate the error signal that is propagated by BP. The methods are discussed and empirically evaluated on a set of benchmark problems.