A similarity-based adaptation of naive bayes for label ranking: application to the metalearning problem of algorithm recommendation

Authors:
Artur Aiguzhinov;Carlos Soares;Ana Paula Serra
Affiliations:
Faculdade de Economia da Universidade do Porto and LIAAD-INESC, Porto, LA;Faculdade de Economia da Universidade do Porto and LIAAD-INESC, Porto, LA;Faculdade de Economia da Universidade do Porto and Centro de Economia e Finanças da Universidade do Porto
Venue:
DS'10 Proceedings of the 13th international conference on Discovery science
Year:
2010

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Abstract

The problem of learning label rankings is receiving increasing attention from several research communities. A number of common learning algorithms have been adapted for this task, including k-Nearest Neighbours (k-NN) and decision trees. Following this line, we propose an adaptation of the naive Bayes classification algorithm for the label ranking problem. Our main idea lies in the use of similarity between the rankings to replace the concept of probability. We empirically test the proposed method on some metalearning problems that consist of relating characteristics of learning problems to the relative performance of learning algorithms. Our method generally performs better than the baseline indicating that it is able to identify some of the underlying patterns in the data.