The　Common　Data　Elements　(CDEs)　standard　of　the　International　organization　for　Standardization　(ISO)　11179　is　commonly　used　in　the　field　of　clinical　data　processing.　The　Biomedical　Research　Integrated　Domain　Group　(BRIDG)　model　is　the　framework　for　biomedical　and　clinical　research.　Mapping　CDEs　to　BRIDG　(also　known　as　CDE　classification)　would　help　with　interoperability　and　data　analysis　in　the　field　of　clinical　research.　That　said,　manually　mapping　CDEs　to　their　corresponding　BRIDG　class　is　highly　time-consuming　and　labor-intensive.　In　this　paper　we　present　a　new　classification　algorithm　along　with　a　new　oversampling　method.　Our　algorithm　uses　the　Term　Frequency-Inverse　Document　Frequency　(TF-IDF)　as　the　feature　representation　method.　By　assigning　different　weights　to　various　attributes,　we　enable　more　important　attributes　to　perform　more　important　roles　during　the　mapping　process.　In　addition,　the　oversampling　method　generates　every　new　attribute　in　the　minor　class　by　picking　the　length　and　setting　the　word　of　the　new　attribute　according　to　the　existing　training　set.　Our　research　outcomes　demonstrate　significant　contributions　to　the　field　in　the　following　ways:　(1)　Generation　of　a　new　CDE　classification　algorithm　that　outperforms　existing　algorithms　in　the　literature,　including　the　Random　Forest　Classifier,　Linear　Support　Vector　Classification　(SVC),　Multinomial　Naive　Bayes　(NB),　Logistic　Regression,　and　Long　Short-Term　Memory　(LSTM)　networks,　in　terms　of　accuracy,　precision,　recall,　and　F-1　score　measures.　(2)　Generation　of　a　new　oversampling　method　able　to　improve　CDE　classification　accuracy　for　Random　Forest　and　Multinomial　NB.　(3)　Our　classification　algorithm　employs　two　novel　attributes,　namely　＇Data　Element　Preferred　Definition＇　and　＇Document,＇　which　are　more　efficient　at　classifying　CDEs　than　the　six　attributes　traditionally　selected　by　domain　experts.　©　2021　IEEE.