Rear-end　crashes　are　a　major　type　of　traffic　crashes　in　the　U.S.　Of　practical　necessity　is　a　comprehensive　examination　of　its　mechanism　that　results　in　injuries　and　fatalities.　Decision　table　(DT)　and　Naive　Bayes　(NB)　methods　have　both　been　used　widely　but　separately　for　solving　classification　problems　in　multiple　areas　except　for　traffic　safety　research.　Based　on　a　two-year　rear-end　crash　dataset,　this　paper　applies　a　decision　table/Naive　Bayes　(DTNB)　hybrid　classifier　to　select　the　deterministic　attributes　and　predict　driver　injury　outcomes　in　rear-end　crashes.　The　test　results　show　that　the　hybrid　classifier　performs　reasonably　well,　which　was　indicated　by　several　performance　evaluation　measurements,　such　as　accuracy,　F-measure,　ROC,　and　AUC.　Fifteen　significant　attributes　were　found　to　be　significant　in　predicting　driver　injury　severities,　including　weather,　lighting　conditions,　road　geometry　characteristics,　driver　behavior　information,　etc.　The　extracted　decision　rules　demonstrate　that　heavy　vehicle　involvement,　a　comfortable　traffic　environment,　inferior　lighting　conditions,　two-lane　rural　roadways,　vehicle　disabled　damage,　and　two-vehicle　crashes　would　increase　the　likelihood　of　drivers　sustaining　fatal　injuries.　The　research　limitations　on　data　size,　data　structure,　and　result　presentation　are　also　summarized.　The　applied　methodology　and　estimation　results　provide　insights　for　developing　effective　countermeasures　to　alleviate　rear-end　crash　injury　severities　and　improve　traffic　system　safety　performance.　(C)　2016　Elsevier　Ltd.　All　rights　reserved.