JUCS - Journal of Universal Computer Science 19(4): 502-520, doi: 10.3217/jucs-019-04-0502
Boosting-based Multi-label Classification
Tomasz Kajdanowicz‡, Przemyslaw Kazienko‡‡ Wrocław University of Technology, Wrocław, Poland
Corresponding author: Tomasz Kajdanowicz ( tomasz.kajdanowicz@pwr.wroc.pl ) © Tomasz Kajdanowicz, Przemyslaw Kazienko. This article is freely available under the J.UCS Open Content License. Citation:
Kajdanowicz T, Kazienko P (2013) Boosting-based Multi-label Classification. JUCS - Journal of Universal Computer Science 19(4): 502-520. https://doi.org/10.3217/jucs-019-04-0502 |  |
Abstract
Multi-label classification is a machine learning task that assumes that a data instance may be assigned with multiple number of class labels at the same time. Modelling of this problem has become an important research topic recently. This paper revokes AdaBoostSeq multi-label classification algorithm and examines it in order to check its robustness properties. It can be stated that AdaBoostSeq is able to result with quite stable Hamming Loss evaluation measure regardless of the size of input and output space.
Keywords
multi-label classification, boosting, AdaBoostSeq, machine learning