@misc{Kajdanowicz_Tomasz_Multi-label, author={Kajdanowicz, Tomasz and Kazienko, Przemysław}, howpublished={online}, publisher={Zielona Góra: Uniwersytet Zielonogórski}, language={eng}, abstract={A framework for multi-label classification extended by Error Correcting Output Codes (ECOCs) is introduced and empirically examined in the article. The solution assumes the base multi-label classifiers to be a noisy channel and applies ECOCs in order to recover the classification errors made by individual classifiers.}, abstract={The framework was examined through exhaustive studies over combinations of three distinct classification algorithms and four ECOC methods employed in the multi-label classification problem.}, abstract={The experimental results revealed that (i) the Bode?Chaudhuri?Hocquenghem (BCH) code matched with any multi-label classifier results in better classification quality; (ii) the accuracy of the binary relevance classification method strongly depends on the coding scheme; (iii) the label power-set and the RAkEL classifier consume the same time for computation irrespective of the coding utilized; (iv) in general, they are not suitable for ECOCs because they are not capable to benefit from ECOC correcting abilities; (v) the all-pairs code combined with binary relevance is not suitable for datasets with larger label sets.}, type={artykuł}, title={Multi-label classification using error correcting output codes}, keywords={machine learning, supervised learning, multi-label classification, error-correcting output codes, ECOC, ensemble methods, binary relevance, framework}, }