TY - GEN A1 - Kajdanowicz, Tomasz A1 - Kazienko, Przemysław A2 - Cordón, Oskar - ed. A2 - Kazienko, Przemysław - ed. PB - Zielona Góra: Uniwersytet Zielonogórski N2 - 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. N2 - 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. N2 - 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. L1 - http://zbc.uz.zgora.pl/Content/58995/AMCS_2012_22_4_4.pdf L2 - http://zbc.uz.zgora.pl/Content/58995 KW - machine learning KW - supervised learning KW - multi-label classification KW - error-correcting output codes KW - ECOC KW - ensemble methods KW - binary relevance KW - framework T1 - Multi-label classification using error correcting output codes UR - http://zbc.uz.zgora.pl/dlibra/docmetadata?id=58995 ER -