Shapelets　are　subsequences　of　time-series　that　represent　local　patterns　and　can　improve　the　accuracy　and　the　interpretability　of　time-series　classification.　The　major　task　of　time-series　classification　using　shapelets　is　to　discover　high　quality　shapelets.　However,　this　is　challenging　since　local　patterns　may　have　various　scales/lengths　rather　than　a　unified　scale.　In　this　paper,　we　resolve　this　problem　by　discovering　shapelets　with　multiple　scales.　We　propose　a　novel　Multi-Scale　Shapelet　Discovery　(MSSD)　algorithm　to　discover　expressive　multi-scale　shapelets　by　extending　initial　single-scale　shapelets　(i.e.,　shapelets　with　a　unified　scale).　MSSD　adopts　a　bidirectional　extension　process　and　is　robust　to　extend　single-shapelets　obtained　by　different　methods.　A　supervised　shapelet　quality　measurement　is　further　developed　to　qualify　the　extension　of　shapelets.　Comprehensive　experiments　conducted　on　25　UCR　time-series　datasets　show　that　multi-scale　shapelets　discovered　by　MSSD　improve　classification　accuracy　by　around　10%　(in　average),　compared　with　single-scale　shapelets　discovered　by　counterpart　methods.