Abstract
Multi-label decision procedures are the target of the supervised learning algorithm we propose in this paper. Multi-label decision procedures map examples to a finite set of labels. Our learning algorithm extends Schapire and Singer’s Adaboost.MH and produces sets of rules that can be viewed as trees like Alternating Decision Trees (invented by Freund and Mason). Experiments show that we take advantage of both performance and readability using boosting techniques as well as tree representations of large set of rules. Moreover, a key feature of our algorithm is the ability to handle heterogenous input data: discrete and continuous values and text data.
Partially supported by project DATADIAB: “ACI télémédecine et technologies pour la santé” and project TACT/TIC Feder & CPER Région-Nord Pas de Calais.
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De Comité, F., Gilleron, R., Tommasi, M. (2003). Learning Multi-label Alternating Decision Trees from Texts and Data. In: Perner, P., Rosenfeld, A. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2003. Lecture Notes in Computer Science, vol 2734. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45065-3_4
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DOI: https://doi.org/10.1007/3-540-45065-3_4
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