Active　learning　uses　a　certain　algorithm　to　select　the　most　useful　samples　for　labeling　to　obtain　a　model　with　better　performance,　thereby　reducing　labeling　costs.　Active　learning　can　be　used　not　only　for　classification,　but　also　for　regression,　named　as　ALR.　Pool-based　sequential　active　learning　refers　to　selecting　one　sample　from　a　pool　for　labeling　in　each　iteration.　For　pool-based　sequential　ALR,　existing　methods　are　guided　by　criteria　such　as　informativeness,　representativeness,　or　diversity　singly,　but　rarely　consider　these　three　criteria　synthetically.　In　this　paper,　we　propose　a　pool-based　sequential　ALR　method　based　on　incremental　cluster　center　selection　(ICS),　which　takes　into　account　the　representativeness　and　diversity.　Besides,　we　further　improve　ICS　by　combining　it　with　informativeness-based　methods,　so　that　the　combined　method　comprehensively　considers　the　above　three　criteria.　Experiments　on　multiple　real　world　datasets　in　various　fields,　using　ridge　regression,　proved　the　effectiveness　of　the　proposed　methods.　©　2022　IEEE.