SciELO - Scientific Electronic Library Online

 
vol.14 issue1Extensions of UML to Model Aspect-oriented Software SystemsCoordinated Tuning of a Group of Static Var Compensators Using Multi-Objective Genetic Algorithm author indexsubject indexarticles search
Home Pagealphabetic serial listing  

Services on Demand

Journal

Article

Related links

Share


CLEI Electronic Journal

On-line version ISSN 0717-5000

Abstract

CHERMAN, Everton Alvares; MONARD, Maria Carolina  and  METZ, Jean. Multi-label Problem Transformation Methods: a Case Study. CLEIej [online]. 2011, vol.14, n.1, pp.4-4. ISSN 0717-5000.

Traditional classification algorithms consider learning problems that contain only one label, i.e., each example is associated with one single nominal target variable characterizing its property. However, the number of practical applications involving data with multiple target variables has increased. To learn from this sort of data, multi-label classification algorithms should be used. The task of learning from multi-label data can be addressed by methods that transform the multi-label classification problem into several single-label classification problems. In this work, two well known methods based on this approach are used, as well as a third method we propose to overcome some deficiencies of one of them, in a case study using textual data related to medical findings, which were structured using the bag-of-words approach. The experimental study using these three methods shows an improvement on the results obtained by our proposed multi-label classification method.

Keywords : machine learning; multi-label classification; binary relevance; label dependency.

        · abstract in Portuguese     · text in English     · English ( pdf )

 

Creative Commons License All the contents of this journal, except where otherwise noted, is licensed under a Creative Commons Attribution License