This　paper　presents　an　effective　method　for　solving　imbalanced　learning.　Imbalanced　learning　is　a　recognition　problem　with　data　with　imbalanced　distributions.　Many　practical　applications,　e.g.,　fraud　identification　and　intrusion　detection,　face　the　critical　problem　of　imbalanced　data.　Most　traditional　methods　use　accuracy　to　evaluate　the　classifier＇s　performance　such　that　it　is　difficult　to　improve　the　accuracy　of　minority　classes.　In　order　to　address　the　problem　of　imbalanced　data,　this　proposed　methods　has　two　advantages.　First,　a　new　strategy　of　weight　updating　is　proposed,　called　G-weighting　scheme.　It　assigns　different　weights　to　different　kinds　of　training　samples　such　that　these　weights　can　represent　the　importance　of　training　samples　and　the　case　that　the　classifier　favors　majority　classes　can　be　avoided.　Secondly,　a　cascading　framework　is　proposed　to　improve　accuracy　of　each　class　and　get　further　balanced　performance　between　accuracy　and　G-means　measure.　In　addition,　this　proposed　method　has　the　advantages　of　fast　learning　speed　and　good　generation.　Experimental　results　on　15　binary-class　data　and　5　multi-class　data　from　KEEL　repository　show　that　this　proposed　method　can　get　better　results　than　other　state-of-the-art　methods.　©　2017　IEEE.