Purpose　Image　classification　is　becoming　a　supporting　technology　in　several　image-processing　tasks.　Due　to　rich　semantic　information　contained　in　the　images,　it　is　very　popular　for　an　image　to　have　several　labels　or　tags.　This　paper　aims　to　develop　a　novel　multi-label　classification　approach　with　superior　performance.　Design/methodology/approach　Many　multi-label　classification　problems　share　two　main　characteristics:　label　correlations　and　label　imbalance.　However,　most　of　current　methods　are　devoted　to　either　model　label　relationship　or　to　only　deal　with　unbalanced　problem　with　traditional　single-label　methods.　In　this　paper,　multi-label　classification　problem　is　regarded　as　an　unbalanced　multi-task　learning　problem.　Multi-task　least-squares　support　vector　machine　(MTLS-SVM)　is　generalized　for　this　problem,　renamed　as　multi-label　LS-SVM　((MLS)-S-2-SVM).　Findings　Experimental　results　on　the　emotions,　scene,　yeast　and　bibtex　data　sets　indicate　that　the　(MLS)-S-2-SVM　is　competitive　with　respect　to　the　state-of-the-art　methods　in　terms　of　Hamming　loss　and　instance-based　F1　score.　The　values　of　resulting　parameters　largely　influence　the　performance　of　(MLS)-S-2-SVM,　so　it　is　necessary　for　users　to　identify　proper　parameters　in　advance.　Originality/value　On　the　basis　of　MTLS-SVM,　a　novel　multi-label　classification　approach,　(MLS)-S-2-SVM,　is　put　forward.　This　method　can　overcome　the　unbalanced　problem　but　also　explicitly　models　arbitrary　order　correlations　among　labels　by　allowing　multiple　labels　to　share　a　subspace.　In　addition,　the　multi-label　classification　approach　has　a　wider　range　of　applications.　That　is　to　say,　it　is　not　limited　to　the　field　of　image　classification.