Study of data transformation techniques for adapting single-label prototype selection algorithms to multi-label learning.

•Data transformation methods for instance selection (IS) in multi-label problems is investigated.•The approach allows the adaptation to multi-label of IS methods for single-label.•The adaptation of LSSm algorithm using binary relevance shows competitive results.•At the moment, the IS is not as advantageous for multi-label as it is for single-label.