A Comparative assessment of Data Mining Algorithms to predict fraudulent firms
Published in IEEE International Conference on Cloud Computing, Data Science & Engineering(Confluence), 2019
Recommended citation: H. Monish and A. C. Pandey, "A Comparative Assessment of Data Mining Algorithms to Predict Fraudulent Firms," 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence), 2020, pp. 117-122, doi: 10.1109/Confluence47617.2020.9057968. https://ieeexplore.ieee.org/document/9057968
In this paper, we compare the classification models of Data mining that can predict the fraudulent firm based on current and past risk factors. Further, we apply the ensemble techniques to improve the models and finally compare the models based on accuracy and compute complexity. The dataset collected is multivariate having 18 attributes that have been considered as risk factors.
Recommended citation: H. Monish and A. C. Pandey, “A Comparative Assessment of Data Mining Algorithms to Predict Fraudulent Firms,” 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence), 2020, pp. 117-122, doi: 10.1109/Confluence47617.2020.9057968.