@book{scholars19007,
            year = {2023},
            note = {cited By 0},
           pages = {227--238},
         journal = {Explainable Artificial Intelligence (XAI): Concepts, enabling tools, technologies and applications},
       publisher = {Institution of Engineering and Technology},
           title = {Enterprise knowledge graphs using ensemble learning and data management},
            isbn = {9781839536960; 9781839536953},
          author = {Kit, J. L. O. W. and Asirvadam, V. S. and Hassan, M. F. B.},
        abstract = {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). {\^A}{\copyright} The Institution of Engineering and Technology 2023.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85181627137&partnerID=40&md5=b7a38ffbbf95c9d35942e05888956f35}
}