TY  - BOOK
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85181627137&partnerID=40&md5=b7a38ffbbf95c9d35942e05888956f35
AV  - none
N2  - Ensemble model is made of a set of models that integrate various type supervised for form classifier to increase or boast prediction consistency. This chapter introduced improved algorithm framework for supervised learning which takes the best three classifiers out of six and combine to produce enhanced ensemble model using uniform voting approach. The proposed technique is tested on PIMA Indian Diabetes dataset and showed superior performance compared to classification tree-based extended techniques (e.g., Random Forest and AdaBoost). The new structured formulated ensemble framework introduced also tend to be invariant to size of fold during validation process (k-fold validation). © The Institution of Engineering and Technology 2023.
PB  - Institution of Engineering and Technology
N1  - cited By 0
SP  - 227
ID  - scholars19007
TI  - Enterprise knowledge graphs using ensemble learning and data management
SN  - 9781839536960; 9781839536953
Y1  - 2023///
EP  - 238
A1  - Kit, J.L.O.W.
A1  - Asirvadam, V.S.
A1  - Hassan, M.F.B.
ER  -