Imbalanced　data　are　ubiquitous　in　real-world　datasets.　This　study　investigate　imbalanced　data　distribution　for　binary　classification,　i.e.,　where　the　number　of　majority　class　instances　is　significantly　greater　than　the　number　of　minority　class　instances.　It　is　assumed　that　traditional　machine　learning　algorithms　attempt　to　minimize　empirical　risk　factors,　and,　as　a　result,　the　classification　accuracy　of　the　minority　is　often　sacrificed.　However,　people　are　often　interested　in　the　minority.　Various　data-level　methods,　such　as　over-　and　under-sampling,　and　algorithm-level　methods,　such　as　ensemble,　cost-sensitive,　and　one-class　learning,　have　been　proposed　to　improve　classifier　performance　with　an　imbalanced　data　distribution.　Based　on　such　methods,　this　study　proposed　a　hybrid　approach　to　deal　with　imbalanced　data　problem　that　comprises　data　preprocessing,　clustering,　data　balancing,　model　building,　and　ensemble.　©　2018　Association　for　Computing　Machinery.