There　has　been　considerable　interest　in　boosting　and　bagging,　including　the　combination　of　the　adaptive　techniques　of　AdaBoost　with　the　random　selection　with　replacement　techniques　of　Bagging.　At　the　same　time　there　has　been　a　revisiting　of　the　way　we　evaluate,　with　chance-corrected　measures　like　Kappa,　Informedness,　Correlation　or　ROC　AUC　being　advocated.　This　leads　to　the　question　of　whether　learning　algorithms　can　do　better　by　optimizing　an　appropriate　chance　corrected　measure.　Indeed,　it　is　possible　for　a　weak　learner　to　optimize　Accuracy　to　the　detriment　of　the　more　reaslistic　chance-corrected　measures,　and　when　this　happens　the　booster　can　give　up　too　early.　This　phenomenon　is　known　to　occur　with　conventional　Accuracy-based　AdaBoost,　and　the　MultiBoost　algorithm　has　been　developed　to　overcome　such　problems　using　restart　techniques　based　on　bagging.　This　paper　thus　complements　the　theoretical　work　showing　the　necessity　of　using　chance-corrected　measures　for　evaluation,　with　empirical　work　showing　how　use　of　a　chance-corrected　measure　can　improve　boosting.　We　show　that　the　early　surrender　problem　occurs　in　MultiBoost　too,　in　multiclass　situations,　so　that　chance-corrected　AdaBook　and　Multibook　can　beat　standard　Multiboost　or　AdaBoost,　and　we　further　identify　which　chance-corrected　measures　to　use　when.